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BI Best Practices Business Intelligence

Ad hoc Analysis vs Canned Reports: Which One Should You Use?

If you’re a regular user of any type of data dashboard or analytics system, you’ve likely encountered a serious question about how to produce reports. Do you go with a canned report, or should you create ad-hoc analysis? Both approaches have their virtues, and your circumstances will often dictate which one you use. Let’s take a closer look at the question itself and the available options to make sure you make the right decision the next time the choice comes up.

What is the Difference?

Before getting too involved with this issue, it’s wise to clarify what we mean by canned versus ad hoc. A canned product is one that either:

  • Comes right out the box with your analytics program
  • Is based on a template someone at your organization has created or acquired

Generally, canned reports have limitations. In particular, you usually can’t squeeze more out of them than your BI dashboard allows you to. This can seriously limit customization.

Conversely, ad-hoc analysis is more of an off-the-cuff approach. This generally involves more labor and time because you have to put time into creating and formatting the report, even if your preferred dashboard provides you the necessary tools.

Pros and Cons of Ad Hoc Analysis

Time and labor are the biggest traps when trying to do anything on an ad-hoc basis. Without rails to guide you, the process you use can develop a big of mission creep. As you fiddle around with the knobs on the dashboard, you’ll find that time can get away from you. That can become a major problem, especially in situations where the first commandment of the job is to just get the report done in a timely manner.

Ad hoc analysis has the virtue of specificity, though. Depending on the nature of the report, it can be helpful to take the time to develop deep dives on specific topics. This is, after all, one of the joys of living in the age of Big Data. Most dashboards are equipped for producing on-the-fly items, and you can generate some impressive results in surprisingly little time once you know where all the controls are located.

The learning curve, however, can be a challenge. If you’re dealing with team members who often resist taking initiative or who don’t pick up tech stuff easily, this can create a barrier. Sometimes, giving them a way to can their reports is just the most painless solution.

Pros and Cons of Canned Analysis

Far and away, the biggest pro of using a canned method is speed. In many cases, you only need to verify the accuracy of the numbers before you click an onscreen button. A report can be spit out in no time, making it very efficient.

One major downside of this approach is that people can tune out when they read canned reports. Especially if you’re putting a work product in front of the same folks every few weeks or months, they can flat-out go blind to the repetitive appearance of the reports.

A big upside, though, is that canned solutions reduce the risk of user errors. Particularly in a work environment where folks may not be savvy about tech or layout and design, it’s often best to have as close to a one-click solution in place. This reduces the amount of technical support required to deliver reports, and it can help folks develop confidence in using the system. Oftentimes, people will begin to explore the options for creating ad-hoc analysis once they’ve had some success with the safer and more boring canned option.

In some cases, canned is the only option. For example, a company that has to produce reports for compliance purposes may have to conform to very specific guidelines for what the work product is formatted. It’s best not to stray under such circumstances, especially if your organization has a track record of generating such reports without issue.

The Fine Art of Choosing

As previously noted, your situation will often be the main driver of what choice you might make. If you’re working on a tough deadline, going the canned route has the benefit of making sure you can deliver a minimally acceptable product on time. There’s a good chance literally no one will be impressed with your efforts, but at least the report will be done.

Some topics deserve more attention than a canned product can afford. As long as you’re confident you have the required skills, you should consider putting them to work to do a deep dive in your report. This affords you the chance to tailor graphics and data tables to your audience. Especially when you’re first introducing folks to a particular topic or a unique dataset, this level of extra attention can be a difference-maker.

There is no perfect answer to the timeless question of canned versus ad hoc. Every situation has its parameters, and it’s prudent to be considerate of those requirements when you make your choice. With a bit of forethought about your approach, however, you can make sure that you’ll deliver a work product that will exceed the expectations of your target audience.

Read more similar content here.

Categories
BI Best Practices Business Intelligence

Why BI Projects Tend to Have a High Failure Rate – Ensuring Project Success

BI projects can begin with a simple goal, but can easily go astray. BI work often involves multiple moving parts and actors. These projects can become complex, containing many dependent pieces. Critical decisions made at the wrong level can lead the plan to chaos. Additionally, timelines are sometimes aggressive and don’t fully account for delays. There are many ways that the project can fail, resulting in money wasted, below we will discuss the top three reasons why they tend to fail. Knowing these failure points is a major step toward ensuring BI project success.

Lack of Communication Between IT and Management Risks Your Project’s Success

Miscommunication can often jeopardize project success. It stems from failure to comply with ambiguous requirements involving a project. Thus, abject business outcomes can occur creating a lack of trust in the workplace.

Some examples of miscommunication are…

  • The target isn’t made apparent
  • Insights from the data aren’t clearly stated
  • The users’ wants and expectations aren’t known.

The blame usually falls upon the IT team because of their inability to communicate. Under 10% of IT leaders believe that they effectively communicate with non-IT colleagues.

There can also be fault found in management not promoting effective communication.  For instance, creating dedicated communication activities or displaying the importance of IT communication. Project metrics sometimes aren’t in place, so the expectations aren’t stated. There is also an unrealistic expectation from management that there won’t be delays. Most people don’t work at full capacity every single day of the project. A better timeline needs to be in place for unexpected bumps in the road.

This lack of conversation causes teams to be held back from their full potential. A feasible solution is to hire a communications director for the IT department. Their needs will be heard and conveyed to management, and vice versa. This will bridge the gap of being “lost in translation” between company officers and their IT team. If that option is out of the picture, have more executive support in the IT projects for there to be guidance.

Time to Value of BI Projects Are Unsatisfactory

BI project failure rate is upwards to 80% according to Gartner. Unclear business targets leave analysts with questions like…

  • What do I present to my audience?
  • Are we using the most desirable data?
  • What data should I cultivate for this project?

Along with answering those propositions, data quality issues can emerge. Dirty data can cause BI teams to become stagnant, due to the amount of data prep necessary. Increases in time-to-value occur because of the lack of alignment between business goals and the data. Checkpoints of project progression on what needs to be accomplished should happen quarterly. When implementing or a scaling a solution it can be a long process that causes users to grow impatient. This is caused by the inability to quickly learn and adapt new BI tools or the lack of qualified personnel. Once a product/update is on the market, the cost to benefit analysis comes in. Most BI projects take a while to see the intended profits, this can lead to discouragement.

If requirements weren’t formally stated, the final product can become a big flop. All the time spent on the project won’t have much value.

Data Issues

Some questions to keep in mind when working on BI projects are:

  • Where does the data come from?
  • What is the validity?
  • Do the data sets make logical sense to analyze?

The data needs to be sanitized/filtered, in order to achieve business objectives. Companies collect mass amounts of data and have numerous ways to analyze it. Focusing on the target allows this process to become simplified. Without a fixation on the target goal, “data paralysis” can occur and the objective could be lost. Data paralysis is defined as, “over analyzing a situation to where no decisions are made, causing any forward movement to halt.”

Finding a way to harness specific data that is relevant to the business need; insights can be drawn. Key points are highlighted and then presented through data visualization. There should be a focus on audience and what they need to know when presenting your insights.

Call to Action

Although there are many ways for a BI project to be unsuccessful; lack of communication, time to value issues, and data issues are the top causes. A way to solve these problems is to hire a project manager that has a background in BI. That individual will be able to communicate with the IT department and executives to create an attainable goal between both parties. The project manager can adjust the timeline allowing the IT department to have adequate time to complete the project and reap the rewards. 

Assuring effective communication will allow setting quality expectations and goals to become easier.  Avoiding data quality issues and slowdowns will sidestep schedule delays. The timeline for the BI project should be flexible, issues are bound to happen. It is more likely for human error to arise than technology error, so correcting your team’s actions will save project time and money.

Read more similar content here.

Categories
BI Best Practices Big Data Business Intelligence

What is Ad Hoc Reporting? – Ad Hoc Reporting for Tableau, PowerBI, Excel

What is Ad-hoc Reporting?

If you heard someone using the term “ad hoc reporting” for the first time, you might think they’re using another language, or are at least missing a few letters.  Well, that’s partly true.

Ad-hoc is Latin for “as the occasion requires.”  When you see “ad-hoc,” think “on-the-fly”. Ad-hoc reporting is a model of business intelligence (BI) in which reports are created and shared on-the-fly, usually by nontechnical business intelligence users. These reports are often done with a single specific purpose in mind, such as to provide data for an upcoming meeting, or to answer a specific question.

Under the ad-hoc model, users can use their reporting and analysis solution to answer their business questions “as the occasion requires,” without having to request help from a technology specialist. A key feature of ad-hoc reporting is that it enables, and embodies, self-service BI in most enterprises. Ad-hoc reports can be as simple as a one page data table or as complex and rich as interactive tabular or cross-tab reports with drill-down and visualization features–or present themselves in the form of dashboards, heat maps, or other more advanced forms.

With ad-hoc reports, all the technical user does is set up the BI solution, ensure the data is loaded and available, set security parameters and give the users their account logins. From that point on, the actual reports are created by business end-users.

Ad hoc reporting stands in contrast with managed reporting, where the technical user–the report developer–creates and distributes the report. As you may have guessed already, if your BI tool of choice supports ad-hoc reports, it will be a big time saver for your technical report developers.

Who Uses These Types of Reports?

This depends in large part on a) the type of ad-hoc solution employed, b) the needs of the end-user and c) the user’s confidence with the solution.

The most common creators of ad-hoc reports are business users and departmental data analysts. In some BI shops, ad-hoc reporting access can be shared outside the organization with business partners and outside auditors, who may need secure access to this information.

What is Ad-Hoc Reporting and Analysis Used For?

Ad hoc analysis is performed by business users on an as-needed basis to address data analysis needs not met by the business’s established, recurring reporting that is already being produced on a daily, weekly, monthly or yearly basis. The benefits of self-service BI conducted by ad hoc analysis tools include:

  • More current data: Ad hoc analysis may enable users to get up-to-the-minute insights into data not yet analyzed by a scheduled report.
  • New reports produced in record time: Since these reports may be single-use, you want to produce them as inexpensively as possible. Ad-hoc report features in a BI tool allow users to sidestep the lengthy process that can go into a normal report, including design work, development, and testing.
  • Line-of-business decisions can be made faster: Allowing users — typically, managers or executives — access to data through a point-and-click interface eliminates the need to request data and analysis from another group within the company. This capacity enables quicker response times when a business question comes up, which, in turn, should help users respond to issues and make business decisions faster.
  • IT workload reduction: Since ad hoc reporting enables users to run their own queries, IT teams field fewer requests to create reports and can focus on other tasks.

Although most ad hoc reports and analyses are meant to be run only once, in practice, they often end up being reused and run on a regular basis. This can lead to unnecessary reporting processes that affect high-volume reporting periods. Reports should be reviewed periodically for efficiencies to determine whether they continue to serve a useful business purpose.

The Goal of Ad-hoc Report Creation

Ad hoc reporting’s goal is to empower end-users to ask their own questions of company data, without burdening IT with the task of creating a myriad of reports to serve different functions and purposes. Ad-hoc reporting therefore makes the most sense when a large number of end-users need to see, understand, and act on data more or less independently, while still being on the same page as far as which set of numbers they look at.

For example, a company with a large outside sales force would be the perfect fit for ad-hoc reporting. Each sales rep can set up his own report for his territory, showing performance against sales goals, orders taken, number of visits to each client, etc., in a format that makes the most sense to him. And just as importantly, the numbers used are pulled from the same data sources as the rest of the company, thereby promoting consistency and minimizing surprises at the end of the quarter.

Top 3 Benefits of Ad Hoc Reporting

1. Empowering End-Users to Build Their Own Reports Saves Money and Time

In a study of over 100 analytics managers, 50% of the team’s time was spent working on ad hoc reporting requests, rather than creating new dashboards and analysis. Since the vast majority of ad hoc reporting is single use, then discarded, that is a major waste of valuable analyst time. Why are the analysts doing this? Usually it’s because they’re the only ones in the company with the skills to do so. But this is a huge misuse of resources. That expensive analyst time that should be spent on building re-usable analyses that can benefit a large population of users. Putting that ability into the hands of all the users, with simple ad hoc reporting tools, accomplishes three key things:  1) It frees up expensive analysts and keeps them from performing unchallenging tasks. 2) it makes end users feel empowered and self sufficient 3) it saves the time wasted for a business user to explain their requirements to an analyst and lets them get straight to work.

2. Encouraging Users to Explore Data Increases Data Discovery

Intuitive ad hoc reporting stimulates information sharing among departments. It enables trend recognition along with any potential relevant correlations based on the immediate availability of accurate, up-to-date data. By increasing the chance for discovery, you increase the chances of finding things like inconsistencies, new revenue markets, and more. Giving end users flexible, easy-to-use ad hoc reporting tools makes them feel empowered, lets them be more hands-on with their data. Which would you trust more? A report done by someone in another division, in a different time zone, or something you put together and tested yourself?

3. Ad Hoc Reporting Streamlines Decision-Making

Of all the things ad hoc reporting can be, at its core, it’s a lever to improve decision-making. Reports give a snapshot of the current state of the business through a specific lens – sales, marketing, performance, and other measures. Organized into a sharable and easy-to-read format, all employees can have the same resources and knowledge necessary for swift action. It makes data analysis a team effort.

Benefits of a Web-based Solution

Get critical information to the right people at the right time – Self-service results plus automatic scheduling/delivery of information let you facilitate timely decision making. Users get the information they need when they need it to answer critical, real-time questions.

Flexibility for constantly changing environments – Business needs to evolve. Answers to changing business questions become more critical. It’s impossible to predict what questions and answers users may need in the future.

Saves training costs and time – Streamlines users’ access to critical information. Easy-to-use wizards allow users to get up and running quickly, requiring less time to learn the application and providing clear guidance and saving time to build reports.

Encourages collaboration and information sharing – Users can easily create, organize, publish and make reports available to other users via the Web for on-demand viewing.

Reduces IT workload – The Web-based reporting application itself can be deployed quickly for widespread availability to end-users. Once deployed, it empowers users to build the reports themselves anytime they need the information. No waiting for IT report developers to build them.

What to Look For in a Good Ad-hoc Report Solution

Now that you have an understanding of what ad-hoc reports are, a good reporting solution should check all of the specific boxes in your feature list. It should be intuitive and easy to use by both business users and technologists. It should be broadly accessible with a light footprint so that many people can access it. It should be able to deliver the answers to users questions quickly and cleanly. In short, it should be oriented toward self-service BI and should be lightweight, fast, and easy to use.

A good ad hoc reporting solution will offer the following characteristics:

Easy to use. If it is or even appears to be complicated, many end-users will be turned off and user adoption will suffer. For this reason, some of the better ad-hoc solutions available today offer a basic set of intuitive features that are wizard-driven and will look easy even to the proverbial “non-computer person,” while also offering more advanced sets of tools for the user who feels confident.

Robust. Assuming that adoption is not an issue (see the previous point), the ad-hoc solution should offer end-users what they need to see, understand and act upon their data. Far from being a more hi-tech version of Excel, it should offer interactive features like ad-hoc dashboards, drill-down and drill-through, advanced sorting and filtering, rich visualization tools like heat maps, charts and graphs, etc.

Widely accessible. For it to be truly useful, a BI solution (including ad-hoc reporting) should web-delivered and accessible with a browser. Apart from offering familiar navigability, and security, a Web-based solution is available from virtually anywhere and on any device via Internet connection. Another benefit of a Web-based ad-hoc solution is that the system administrator won’t have to set it up individually on every user’s machine: installing it on the server is enough, and all the users need to access it is a simple URL.

Today’s better Web-based ad-hoc solutions are data-source neutral, meaning that they can connect practically out of the box to most of today’s commonly-used data-sources, including databases, Web-services, flat files, etc. This saves the IT department the burden of creating complex metadata structures as the underlying layer, which is time-consuming, cumbersome and expensive.

If you’re a regular user of any type of data dashboard or analytics system, you’ve likely encountered a serious question about how to produce reports. Do you go with a canned report, or should you create ad-hoc reports? Both approaches have their virtues, and your circumstances will often dictate which one you use. Let’s take a closer look at the question itself and the available options to make sure you make the right decision the next time the choice comes up.

What is the Difference?

Before getting too involved with this issue, it’s wise to clarify what we mean by canned versus ad hoc reporting. A canned product is one that either:

  • Comes right out the box with your analytics program
  • Is based on a template someone at your organization has created or acquired

Generally, canned reports have limitations. In particular, you usually can’t squeeze more out of them than your BI dashboard allows you to. This can seriously limit customization.

Conversely, ad-hoc reporting is more of an off-the-cuff approach. This generally involves more labor and time because you have to put time into creating and formatting the report, even if your preferred dashboard provides you the necessary tools.

Pros and Cons of Ad Hoc Analysis

Time and labor are the biggest traps when trying to do anything on an ad-hoc basis. Without rails to guide you, the process you use can develop mission creep. That can become a major problem, especially in situations where the first commandment of the job is to just get the report done in a timely manner.

Ad hoc analysis has the virtue of specificity, though. Depending on the nature of the report, it can be helpful to take the time to develop deep dives on specific topics. This is, after all, one of the joys of living in the age of Big Data. Most dashboards are equipped for producing on-the-fly items, and you can generate some impressive results in surprisingly little time once you know where all the controls are located.

The learning curve, however, can be a challenge. If you’re dealing with team members who often resist taking initiative or who don’t pick up tech stuff easily, this can create a barrier. Sometimes, giving them a way to can their reports is just the most painless solution.

Pros and Cons of Canned Analysis

Far and away, the biggest pro of using a canned method is speed and repeatability. In many cases, you only need to verify the accuracy of the numbers before you click an onscreen button. A report can be spit out in no time, making it very efficient.

One major downside of this approach is that people can tune out when they read canned reports. Especially if you’re putting a work product in front of the same folks every few weeks or months, they can flat-out go blind to the repetitive appearance of the reports.

A big upside, though, is that canned solutions reduce the risk of user errors. Particularly in a work environment where folks may not be savvy about tech or layout and design, it’s often best to have as close to a one-click solution in place. This reduces the amount of technical support required to deliver reports, and it can help folks develop confidence in using the system. Oftentimes, people will begin to explore the options for creating ad-hoc analysis once they’ve had some success with the safer and more boring canned option.

In some cases, canned is the only option. For example, a company that has to produce reports for compliance purposes may have to conform to very specific guidelines for what the work product is formatted. It’s best not to stray under such circumstances, especially if your organization has a track record of generating such reports without issue.

The Fine Art of Choosing

As previously noted, your situation will often be the main driver of what choice you might make. If you’re working on a tough deadline, going the canned route has the benefit of making sure you can deliver a minimally acceptable product on time. There’s a good chance literally no one will be impressed with your efforts, but at least the report will be done.

Some topics deserve more attention than a canned product can afford. As long as you’re confident you have the required skills, you should consider putting them to work to do a deep dive in your report. This affords you the chance to tailor graphics and data tables to your audience. Especially when you’re first introducing folks to a particular topic or a unique dataset, this level of extra attention can be a difference-maker.

There is no perfect answer to the timeless question of canned versus ad hoc. Every situation has its parameters, and it’s prudent to be considerate of those requirements when you make your choice. With a bit of forethought about your approach, however, you can make sure that you’ll deliver a work product that will exceed the expectations of your target audience.

Read more similar content here.

Categories
Big Data Business Intelligence Data Analytics

What Does the Salesforce-Tableau Deal Mean For Customers?

Salesforce Buying Tableau for $15.7 Billion

Salesforce will buy Tableau Software for $15.7 billion in an all-stock deal announced Monday morning. Salesforce is doubling down on data visualization and BI in the purchase of one of the top enterprise technology brands.

The all-stock deal will be the largest acquisition in the history of the San Francisco-based cloud CRM giant. It is more than double the amount Salesforce paid for MuleSoft last year ($6.5 billion).

The acquisition price of $15.7 billion is a premium of more than 30 percent over Tableau’s market value of $10.8 billion as of the previous stock market close. The deal is slated to close in the third quarter. The boards of both companies have approved the acquisition, according to the announcement.

The acquisition comes barely a weekend-after Google announced its massive $2.6 billion acquisition of Looker, which also makes data visualization software for businesses.

The deal is also expected to escalate the competition between Salesforce and Microsoft. The two are already fierce competitors in the CRM arena with Salesforce CRM and Microsoft Dynamics CRM. Salesforce, armed with the Tableau product suite, will now compete with Microsoft’s PowerBI data visualization and business intelligence technology. Tableau and Microsoft have been in a fierce fight the last three years, with Tableau’s stock under pressure.

At $15.7 billion, Salesforce buying Tableau is the largest analytics merger and one of the largest software deals in history.

It combines two leaders in their respective space, Tableau for Data Visualization, and Salesforce, leader in Customer Relationship Management SaaS software.

It’s not surprising Salesforce wanted Tableau. Salesforce, like any other large Saas company, stores a massive amount of business data supplied by its thousands of customers. Naturally, those customers are hungry for advanced analytics on that data, and have been telling Salesforce that.

The risk for Salesforce and the massive amount of data it holds is letting that data flow out of its systems to those of competitors – not for new CRM services – but for Analytics.

Customers desiring analytics for Salesforce Data have a multitude of choices, major players like Microsoft’s PowerBI or any of the hundreds of other analysis platforms. Google searches for “CRM Data Analytics” and its variants number in the thousands per day.

Over the past few years, it’s swallowed Analytics companies like goldfish at a 50’s frat party. Salesforce acquisitions in just the last 2 years included:

  • Mulesoft,
  • BeyondCore,
  • PredictionIO,
  • Griddable.io,
  • MapAnything.

Why is Salesforce Investing in Analytics?

Because data has massive value, both current and potential value in the future. Salesforce knows whoever controls the data inherits that value, and has much greater influence over the customer.

Salesforce isn’t the only one who knows this, many other cloud and SaaS players know this too. The new cloud “land-grab” is actually a data grab, which may prove much more valuable than land over time. Cloud companies are doing everything they can to direct as much data into their clouds, and keep it there. Analytics services a way to keep their customers’ data happily ensconced within their own platform.

In the cloud universe, it’s much better to be a massive player with a strong gravitational pull that draws data toward you, than to see data flowing away from you. That may sound simplistic, but that glacial flow of data, first from the company, then into a SaaS application, then onward to other cloud companies, is what makes or breaks these companies’ fortunes.

Salesforce has turned most of its purchases in Data Analytics into the Einstein platform, which has had a decent reception by the market. However, Einstein has not had the planetary effect of drawing in non-Salesforce data and exists mainly to offer insights on Salesforce’s captive CRM data. Its adoption has not broadened significantly beyond Salesforce data.

The acquisition of BeyondCore promised augmented analytics into the portfolio by way of Salesforce Einstein Discovery. In this regard, the Tableau acquisition is good for Salesforce from a product perspective, while also a good move for Tableau shareholders.

There is some obvious overlap in the product portfolios. Tableau had acquired Emperical Systems to bolster its augmented analytics, which will likely be slowed or sidelined. The immediate goal for Salesforce and Tableau will be to rationalize duplicate products and improve the integration. We wonder whether Tableau will become the face of the Salesforce analytics apps, which are full cloud products, since Tableau has continued to lag in its browser-based authoring. All this means that it is not necessarily good news for Tableau customers. The reactions on Twitter were decidedly mixed.

Winners and Losers: What does the Salesforce-Tableau deal mean for customers?

Definite Winner: Tableau Shareholders

Potential Winner: SalesForce Customers

Potential Losers: Tableau Customers, Salesforce Shareholders

The initial reaction in markets and on Twitter was strong. Markets soundly rewarded Tableau shareholders with a 35% share price leap the morning the news came out. Salesforce shareholders didn’t fare so well, with their shares dropping 8% on the announcement, but will likely recover as the news spreads.

Both companies have strong, mature cultures. Tableau was multi-platform and connected to multiple datasets. Salesforce, which did buy Mulesoft to connect to other data sources, is likely to maintain Tableau’s mission and approach, but it’ll have to prove it to some folks. However, Tableau has built up a very successful community around its brand, and includes millions of loyal users among its fanbase.

One response on the Tableau community forum likely sums up the concerns by some customers:

“Will we wake up on this date next year and see ‘Tableau Powered by Salesforce,’ and then the next year Tableau becomes nothing more than a checkbox on the Salesforce contract? I have staked my career on this wonderful tool the past few years and truly love it. I just don’t want to see it ruined or fade off into the sunset.”

It will be interesting to watch how Tableau’s roadmap evolves or changes due to its new ownership.

These two deals are just the latest in a series of acquisitions of data analytics companies over the past quarter or two. We’ll cover the others in Part II of this post.

For now, here are some takeaways about all these acquisitions:

  • The Analytics and BI market remains hot, valuations for these companies continue to go up.
  • It’s clear that most of the benefits of these deals will go to the shareholders. However, the CEOs and boards should also be doing their part to make sure the benefits are shared with the customers and loyal users of these technologies. After all, that’s what got them where they are.
  • This isn’t the first consolidation the Analytics industry has seen. In the late 2000s there was a wave of activity as behemoths like SAP, IBM and Oracle gobbled up Business Objects, Cognos and Hyperion, respectively. How did those turn out? Well, the fact that companies like Tableau were born shortly afterward signals that innovation in the bigger companies slowed down after those deals. This paved the way for newer, more agile companies (like Tableau) who listened to the market, and innovated to deliver what it demanded.

If you have a horse in this race, either as a customer, developer or employee of any of the affected companies, drop us a quick comment below to let us know how you’re feeling about this news, and how you think it might affect you.

Categories
Business Intelligence Data Quality

The 3 Things You Need To Know If You Work With Data In Spreadsheets

Microsoft Excel and Google Sheets are the first choice of many users when it comes to working with data. They’re readily available, easy to learn and support universal file formats. When it comes to using a spreadsheet application like Excel or Google Sheets, the point is to present data in a neat, organized manner which is easy to comprehend. They’re also on nearly everyone’s desktop, and were probably the first data-centric software tool any of us learned.  Whether you’re using Excel or Google Sheets, you want your data cleaned and prepped. You want it accurate and consistent, and you want it to elegant, precise, and user-friendly.

But there is a downside. While spreadsheets are popular, they’re far from the perfect tool for working with data. We’re going to explore the Top 3 things you need to be aware of if you work with data in spreadsheets.

While spreadsheet tools are quite adequate for many small to mid-level data chores, there are some important risks to be aware of. Spreadsheets are desktop-class, file-oriented tools which means their entire data contents are stored in volatile RAM while in use and on disk while you’re not using them. That means that between saves, the data is stored in RAM, and can be lost.

Risk #1: Beware of Performance and Data Size Limits in Spreadsheet Tools

Most people don’t check the performance limits in Spreadsheet tools before they start working with them. That’s because the majority won’t run up against them. However, if you start to experience slow performance, it might be a good idea to refer to the limits below to measure where you are and make sure you don’t start stepping beyond them. Like I said above, spreadsheet tools are fine for most small data, which will suit the majority of users.

But at some point, if you keep working with larger and larger data, you’re going to run into some ugly performance limits. When it happens, it happens without warning and you hit the wall hard.

Excel Limits

Excel is limited to 1,048,576 rows by 16,384 columns in a single worksheet.

  • A 32-bit Excel environment is subject to 2 gigabytes (GB) of virtual address space, shared by Excel, the workbook, and add-ins that run in the same process.
  • 64-bit Excel is not subject to these limits and can consume as much memory as you can give it. A data model’s share of the address space might run up to 500 – 700 megabytes (MB), but could be less if other data models and add-ins are loaded.

Google Sheets Limits

  • Google Spreadsheets are limited to 5,000,000 cells, with a maximum of 256 columns per sheet. (Which means the rows limit can be as low as 19,231, if your file has a lot of columns!)
  • Uploaded files that are converted to the Google spreadsheets format can’t be larger than 20 MB and need to be under 400,000 cells and 256 columns per sheet.

In real-world experience, running on midrange hardware, Excel can begin to slow to an unusable state on data files as small as 50mb-100mb. Even if you have the patience to operate in this slow state, remember you are running at redline. Crashes and data loss are much more likely!

(If you’re among the millions of people who have experienced any of these, or believe you will be working with larger data, why not check out a tool like Inzata, designed to handle profiling and cleaning of larger datasets?)

Risk #2:  There’s a real chance you could lose all your work just from one mistake

Spreadsheet tools lack any auditing, change control, and meta-data features that would be available in a more sophisticated data cleaning tool. These features are designed to act as backstops for any unintended user error. Caution must be exercised when using them as multiple hours of work can be erased in a microsecond.

Accidental sorting and paste errors can also tarnish your hard work. Sort errors are incredibly difficult to spot. If you forget to include a critical column in the sort, you’ve just corrupted your entire dataset. If you’re lucky enough to catch it, you can undo it, if not, that dataset is now ruined, along with all of the work you just did. If the data saves to disk while in this state, it can be very hard, if not impossible, to undo the damage.

Risk #3:  Spreadsheets Aren’t Really Saving You Any Time

Spreadsheets are fine if you just have to clean or prep data once, but that is rarely the case. Data is always refreshing, new data is coming online. Spreadsheets lack any kind of repeatable processes and or intelligent automation.

If you spend 8 hours cleaning a data file one month, you’ll have to repeat nearly all of those steps the next time a refreshed data file comes along.

Spreadsheets can be pretty dumb sometimes. They lack the ability to learn. They rely 100% on human intelligence to tell them what to do, making them very labor intensive.

More purpose-designed tools like Inzata Analytics allow you to record and script your cleaning activities via automation. AI and Machine Learning lets these tools learn about your data over time. If you Data is also staged throughout the cleaning process, and rollbacks are instantaneous. You can set up data flows that automatically perform cleaning steps on new, incoming data. Basically, this lets you get out of the data cleaning business almost permanently.

(Excerpt from The Ultimate Guide to Cleaning Data in Excel and Google Sheets)

Categories
Big Data Business Intelligence Data Analytics Data Monetization

7 Ways To Grow Your Business with Data Monetization

It’s estimated that by the year 2020 revenues around the world for big data and business analytics are going to exceed $203 billion. With all this earning potential, it makes sense to want to get your business “in on it.”

One of the best ways to do this is with data monetization. After all, data is the new currency.

In the past, businesses in the information technology sector have always been deriving value from data. However, the ability to effectively use and monetize data is now impacting virtually all types of business.

This means that driving value from data is something you can implement in your own business strategy. What many people may not realize is that this process can be extremely challenging.

As a result, you need to learn some helpful tools and actionable steps you can take to monetize data for your business.

If you are interested in learning more, then keep reading.

1. Decision Architecture

When thinking about analytics, the majority of organizations want to know how their business is performing, along with what information is needed to answer various performance questions. While this can help to inform and to describe what is taking place in the organization, it doesn’t enable any type of action.

Instead, the goal needs to be to capture the decision architecture of specific business problems. Once this is done, you can build analytics capabilities to create a diagnosis that enables decisions and actions. Leaders need to focus on making decisions that are based on data, rather than just answering questions about what already happened.

2. Stop Revenue Leaks

Busy healthcare providers, clinics, and hospitals can easily lose track of the services being rendered. Every procedure has an assigned code and description. Each of these often includes errors.

By using analytics, the organizations can identify patterns associated with procedures and codes, flagging patient invoices for possible errors or even missing charges. Intelligent data use can also help the organizations improve the ROI of their collections process.

3. Data Aggregation

The method that is at the very bottom of the pyramid, but that represents the biggest opportunity to earn, is data aggregation.

This means taking data from various sources, including your business, and merging it together to create a larger, integrated picture. While the data sources on their own may be interesting, when they are combined, they become valuable.

An example of this would be your credit report. The information credit bureaus aggregate, such as the credit cards you have, if you have a mortgage, and if you pay your bills on time, can be sold for a profit.

By aggregating this information into a single report, the information can be sold to interested parties. While there isn’t a lot of money in this, it’s still money.

4. Infer Customer Satisfaction

Many organizations use social media and survey sentiment to understand the levels of customer satisfaction. By combining data from several sources, airlines can now infer how satisfied a customer is based on factors, like where they are sitting.

This process requires information to be aggregated from several sources. However, in the airline example, you can use the information to determine if a customer is going to fly with you again, and if not, offer a free upgrade or other incentives.

5. Embrace a New Revenue Model

Today, data is actively changing relationships companies have with customers. Manufacturers of tangible goods are now supplementing the products they sell with flexible software options and services to offer customers new choices and new revenue streams.

Additionally, these companies are providing much higher levels of personalization. Across several industries, new economic models are starting to be explored – like replacing an auto fleet with self-driving cars.

In this example, rather than selling data, people are going to pay you to solve a problem or to provide answers. This is a unique revenue model.

The value lies in the fact that you have married your data to the mission of a business and solving a problem that businesses have. This is what is going to generate revenue.

6. Detect Piracy and Fraud

Most online retailers sell products on several different websites. Supplemental sales channels typically include eBay, Amazon.com and other online marketplaces maintained by larger retailers, like Best Buy and Walmart.

Selling through these channels is extremely data-intensive, since the customer types, products, and pricing can vary greatly across the channels. In some case, the price discrepancies are so large that they signal possible piracy or fraud.

If you sell across dozens of e-commerce websites, then consider building databases of your own products and your unique pricing. You can then compare this to existing expected pricing data, allowing you to detect stolen goods or suppliers who are mispricing their goods.

With this information, it’s possible to go to the marketplace and make a report stating that they believe someone is selling stolen items.

How Can You Use Data Monetization Methods for Your Business?

Data monetization is an ever-evolving concept that offers opportunities to earn profits by providing information to others. Your business can take advantage of this by utilizing the tips and information here.

The fact is, there are already countless businesses, in all industries, that are currently using data monetization. Now is the time to begin doing so, too, as it offers huge revenue stream potential.

If you are convinced that data monetization is something you want to use for your company, then contact us. We can provide you with help and information about how this process works.

Categories
Big Data Business Intelligence

6 Information And Analytics Trends To Prepare for in 2020 

We are well past the point of realization that large data and advanced analytics solutions are valuable — nearly everyone knows that by now. Actually, there’s no escaping that the increasing dependence on technology. Substantial data has become a modern staple of nearly every sector in retail to manufacturing, and for good reason.

IDC forecasts that if our digital universe or total information content were represented by tablets, then by 2020 they would extend all of the way to the moon over six times. That is equal to 44 trillion gigabytes, or 44 zettabytes of information.

There are lots of reasons why information has been created so quickly — doubling in size every 2 years. Although the demand for reliable information is another the birth of IoT and connected devices is one source. What’s more interesting are the tendencies formed as a result of the options that are digitally-reliant that are more recent. They specifically help form the business, altering business analysts work with information.

Will our future look like? How will we handle all this information? What abilities need to company analysts be focused on developing?

1. Specialization of Job Roles

For quite a while, the information scientist and analyst roles have been universal in character. It is not that specializations did not exist, they have but firms are starting to search for professionals who have industry-specific experience. They want someone well versed specifically in the sort of data they are dealing with.

Everything from financial services to manufacturing and logistics has been upgraded to rely on more digital services and as a result a influx of real time information. There are plenty of opportunities, so livelihood decisions won’t be hurt by picking a specialty, but doing exactly the can. It is important to construct a good CV by working together with businesses and teams that fit a specialty, so choose one.

2. Machine Learning Experience is a Must

From 2020, over 40 percent of data science tasks will be automated. Its capacity and machine learning technologies is a massive driver of that automation. It is for good reason because effective machine learning tools and automation will help extract insights that would otherwise be tricky to find even by skilled analysts.

The whole process is achieved much faster, boosting not just general efficiency but the response time of an organization to certain events.

Data scaling analysis, quantitative analysis, automation resources and, of course, overall machine learning are skills that modern data analysts must try to hone. Talent and the more experience that an analyst has with automation technologies, the more desirable they will be.

3. The Growth of Legislation

GDPR helped to spur the requirement for qualitative information governance, and frankly, it happened so quickly it left many firms scrambling to comply — even still some are fumbling with the idea. But it is not. More lately, that the California Consumer Privacy Act reared its head, that will go into effect in 2020. It won’t be the last either, not by a longshot.

These regulations have a monumental effect on information processing and managing , customer profiling and information security. Businesses are under extreme pressure not only to comprehend the effect on operations but also to comply with the requirements.

Data scientists and analysts who understand the ramifications will help organizations browse the guidelines and are skilled in security and data privacy are in large demand. As regulations come to be, that need will continue to rise making it a viable specialty for current and future professionals.

4. Stay close to the Bleeding Edge

It’s no small accomplishment to stay up-to-date with anything that relates to modern technology. Tools and solutions are growing at absurd prices, new opportunities are being introduced, and many different tendencies take form year after year. But regardless of how difficult it is, information analysts have to continue to stay in the forefront of that growth.

A good analyst may focus but never puts their whole stock in toolset, platform or one technology. Using databases, as an instance, choices may include HBase NoSQL and MongoDB but its most priorities may change over time. Information processing is another skill key to remaining relevant in the analytics area. Professionals will probably be desired by companies, individuals and government offices .

For frameworks and languages, there’s Apache Hadoop, Python, R, SAS and others. However, more importantly, these would be the cases now — right now — and they could shift or alter over time.

It is up to analysts to stay present with all and any options readily available, and that means embracing a state of growth and constant improvement so far as knowledge and abilities are involved.

5. Cloud Computing and Related Mechanics

Software engineers and data scientists are two fields, but that does not necessarily imply overlap does not happen. It certainly does and professionals need to understand that achieving it is a very important part of staying relevant in the market of today.

As the requirement for more liquid and elastic infrastructure develops, scientists and analysts need to comprehend how this relates to current operations and gear. When dealing with possible troubles and performance demands Having the ability to evaluate the load on a machine, for instance, can go a very long way.

It is this concept of knowing infrastructure and the hardware in the helm that will elevate professionals to fresh heights. Substantial data analytics, machine learning, none of these technologies would exist with no cloud computing along with the infrastructure.

Until recently, the focus has been about processes and the instruments that can help attain a better understanding of information stores.

The technology gets more capable and is adopted more publicly, as, the requirement to comprehend the hardware has also become more important. The overlap between software and hardware related jobs and the demand for professionals to comprehend the range of the systems.

6. Basic Business Intelligence Expertise is Key

The data analyst of today kept separate and then is not secured in a tower. In fact, it’s nearly always the opposite that is complete, as scientist interact with decision makers and groups. This means that information professionals have to be able to efficiently communicate complicated issues to professionals.

Communication occurs to be a critical soft skill of company intelligence. But it’s not the only skill required to thrive. SQL programming abilities Tableau such as — and problem-solving are just a couple of examples.

The past and tomorrow’s analysts should have a good foundation in business intelligence.

Growth is Always a Must, however the Right Growth is Key

Obviously, build experience in the industry and information analysts may continue to rise as they take on more projects. But boosting the growth that is perfect, into particular areas and abilities, can assist professionals achieve victory, but also secure opportunities in the area.

An increasing number of organizations deploy information analytics tools to affect their operations and to understand consumer behaviour.

These tools have become more advanced alongside the technology, as time progresses. It is around analysts to comprehend tools and the core systems but also the underpinning hardware

Categories
Business Intelligence Data Quality

Chapter 3 – Cleaning Data with Excel and Google Sheets

How Spreadsheets Became The #1 BI Tool in the World

(Excerpt from The Ultimate Guide to Cleaning Data in Excel and Google Sheets)

Microsoft Excel and Google Sheets are the first choice of many users when it comes to handling large amounts of data. They’re readily available, easy to learn and support universal file formats. When it comes to using a spreadsheet application like Excel or Google Sheets, the point is to present data in a neat, organized manner which is easy to comprehend. They’re also on nearly everyone’s desktop, and were probably the first data-centric software tool any of us learned.

In this eBook, we are going to tell you some of the tips as to how to clean and prep up your data using Excel and Google Sheets, and make it accurate and consistent, and make it look elegant, precise, and user-friendly.

spreadsheet software

Risks of Cleaning Data In Spreadsheets

While spreadsheet tools are quite adequate for many small to mid-level data chores, there are some important risks to be aware of. Spreadsheets are desktop-class, file-oriented tools which means their entire data contents are stored in volatile RAM while in use and on disk while you’re not using them. That means that between saves, the data is stored in RAM, and can be lost.

Spreadsheet tools also lack any auditing, change control, and meta-data features that would be available in a more sophisticated data cleaning tool. These features act as backstops for any unintended user error. Caution must be exercised when using them as multiple hours of work can be erased in a microsecond.

Unnoticed sorting and paste errors can also tarnish your hard work. If the data saves to disk while in this state, it can be very hard, if not impossible, to undo the damage and revert to an earlier version.

Spreadsheets also lack repeatable processes and automation. If you spend 8 hours cleaning a data file one month, you’ll have to repeat nearly all of those steps the next time another refreshed data file comes along. More purpose-designed tools like Inzata Analytics allow you to record and script your cleaning activities via automation. Data is also staged throughout the cleaning process, and rollbacks are instantaneous. You can set up data flows that automatically perform cleaning steps on new, incoming data. Basically, this lets you get out of the data cleaning business almost permanently.

Performance and Size Limits in Spreadsheet Tools

Most folks don’t bother to check the performance limits in Spreadsheet tools before they start working with them. That’s because the majority won’t run up against them. However, if you start to experience slow performance, it might be a good idea to refer to the limits below to measure where you are and make sure you don’t start stepping beyond them. Like I said above, spreadsheet tools are fine for most small data, which will suit the majority of users.

Excel Limits

Excel is limited to 1,048,576 rows by 16,384 columns in a single worksheet.

  • A 32-bit Excel environment is subject to 2 gigabytes (GB) of virtual address space, shared by Excel, the workbook, and add-ins that run in the same process.
  • 64-bit Excel is not subject to these limits and can consume as much memory as you can give it. A data model’s share of the address space might run up to 500 – 700 megabytes (MB), but could be less if other data models and add-ins are loaded.

Google Sheets Limits

  • Google Spreadsheets are limited to 5,000,000 cells, with a maximum of 256 columns per sheet. (Which means the rows limit can be as low as 19,231, if your file has a lot of columns!)
  • Uploaded files that are converted to the Google spreadsheets format can’t be larger than 20 MB and need to be under 400,000 cells and 256 columns per sheet.

In real-world experience, running on midrange hardware, Excel can begin to slow to an unusable state on data files as small as 50mb-100mb. Even if you have the patience to operate in this slow state, remember you are running at redline. Crashes and data loss are much more likely!

If you believe you will be working with larger data, why not check out a tool like Inzata, designed to handle profiling and cleaning of larger datasets?

(Excerpt from The Ultimate Guide to Cleaning Data in Excel and Google Sheets)

Categories
Business Intelligence Data Analytics

What is the Difference Between Business Intelligence, Data Warehousing, and Data Analytics?

When listening to discussions of many of the core concepts of the big data world, it often can feel like being caught in a hurricane of technobabble and buzzwords. Three of the most relevant concepts to understand, though, are data warehousing, data analysis, and business intelligence (BI).

Individually, each of these concepts engenders one-third of an overall process. When that process comes together, a company can more efficiently collect data, analyze it and turn it into actionable information for decision-makers at all levels of an operation.

The What

Data warehousing is the most straightforward of the three concepts to understand. As the term suggests, it’s the process of taking collected data in a company and storing it in places where it can be kept secure and accessible. This means having access to either on-site database servers or off-site cloud storage platforms.

Data analysis is the process of scanning through the available data an organization has in order to produce insights. Many people misuse this concept interchangeably with BI. The distinction is that data analysis tools help professionals handle the tasks of:

  • Acquiring data from sources
  • Prepping data for analysis
  • Confirming data integrity
  • Identifying statistically grounded methods for gaining insights
  • Using computing resources to rapidly cull massive amounts of data
  • Iterating through permutations of statistical models to generate insights
  • Verifying that any generated insights are statistically valid

Business intelligence is about taking the raw insights gained using those data analysis tools and turning them into actionable information. BI platforms are designed to provide visualizations and data to stakeholders. For example, a U.S. retailer might offer its buyers in China real-time data streams of insights derived from scanning millions of influencers’ feeds on Twitter, Instagram, Facebook and other social media platforms. This allows the buyers to look at the insights and quickly make decisions about what’s likely to sell well in the upcoming fashion season.

The How

All of this work calls for the support of folks who have experience in working with computing resources at large scales. There’s a lot more going on here than simply putting entries into a spreadsheet. The industry employs plenty of data scientists, computer programmers and IT professionals. Likewise, individuals with business backgrounds in consulting are often in high demand.

From end to end, a company has to build its training and hiring practices around fostering a culture that values big data and insights. Building such a culture often presents its own set of challenges, as many people prefer to make choices based on tastes, gut reactions and “eye tests.”

If you want an insight into how this process unfolds, look no further than the world of professional baseball. Few sports are now as driven by analytics as baseball. Starting at the turn of the century, small clubs that were strapped for cash began hunting for market inefficiencies. Two decades later, everyone in the business is using data analytics tools to make decisions. In 2019, the Houston Astros announced they were cutting their scouting department significantly while adding more people in analytics.

The Why

One of the classic examples of how statistically driven insights can defy expectations is the so-called Monty Hall problem. The original version of the show “Let’s Make a Deal” featured a game where a contestant had to choose one of three doors to win a prize like a new car. Behind one door was something no one wanted, such as a goat. Another door hid the car, and a third one hid a lesser prize.

After the contestant picked a door, the host would reveal what was behind one of the other doors. For the sake of dramatic tension, the host never showed the goat or the car in the first reveal. The host then would ask, “Do you want to change your pick?”

According to volumes of computer simulations and PhD-level stats papers, the answer should always be “yes.” By switching, the contestant improves their chance of winning from 1/3 to 2/3.

If that feels wrong to you, don’t feel bad. The answer is not intuitive. Most people assume the contestant has somewhere between a 1/3 and 1/2 chance when switching. Thousands of respected mathematicians even tried to refute the solution.

Lots of business decisions are basically the Monty Hall problem scaled into the thousands, millions or even billions. There are plenty of doors to pick from, and the goats far outnumber the cars. Also, you’re competing against numerous other contestants simultaneously.

Unless you need to pay a dowry, you probably don’t want that many goats. How do you improve your chances of finding the winning prize? You embrace the value of data warehousing, data analysis and business intelligence.

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Guide to Cleaning Data with Excel & Google Sheets Book Cover by Inzata COO Christopher Rafter