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Category: Big Data

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Big Data

Your Data Analysts Need These 4 Qualities For Optimal Success

Data analysis has become a highly attractive job. If you’re thinking about going in the field, you’re probably wondering what traits make someone a good data analyst. Let’s take a look at four key qualities that define the best professionals in the industry.

Relentless Curiosity

At its core, data analysis is about trying to take a deep dive into information that doesn’t always lend itself to easy answers. You have to want to get to the answers, and you have to be willing to think hard about how to create experiments that will get you there. People in the data analysis world are always wondering:

  • Why things happen in the first place
  • How processes can be improved
  • If we’re really getting the whole picture on a topic
  • Where the missed opportunities are
  • If there’s a different way to take action to solve a problem
  • How deeply an issue can be understood

Drilling through data to get at insights calls for a lot of work. People who succeed as analysts tend to have an abiding discomfort with letting things go. The problem doesn’t go away at the end of the workday, and they’re often the folks who have “ah-ha!” moments in the middle of the night.

Willingness to Question

Curiosity means nothing if it stops when it comes up against a wall. In the early days of the analytics revolution in pro baseball, for example, ridicule was the common response that analysts were met with when they asked the simple question of whether there was a better way to score a run. The revolution persisted because people involved with the sport, especially on the stats side of things, continued to ask a simple question: “Are we even asking the right questions?”

To get to the point you question the very questions other people are asking requires a certain degree of courage. People don’t like to be questioned, and the default response by many analysts has been to just say you should follow the numbers where they take you.

But it takes more than that. Many folks who find themselves in analytics as a profession have somewhat contrarian instincts. They’re not fans of following the herd, and they’re often convinced the herd is going to its doom.

The contrarian worldview has become so pervasive in analytics that the financial world even considers it a distinct form of investing called contrarianism. When the global financial markets collapsed in 2007, for example, the contrarians made billions because they had:

  • Asked the questions others weren’t asking
  • Gathered the data to figure out what was actually happening
  • Followed the numbers to the only logical conclusion
  • Made aggressive decisions based on the best data available at the time

It takes a special type to tell decision-makers that they’re making decisions the wrong way.

Commitment to Learning

The data analysis industry depends on evolution in both statistics and computing. Keeping up with the pace of those developments is not an easy task, and the best analysts tend to be folks who read a lot. They want to keep up with new studies, technological innovations and shifting attitudes. If you’re the kind of person who wants to spend a weekend learning a new programming language or reading up on fresh research, a career in analysis might be for you.

Calling an analyst a lifelong learner would be an understatement. They might not all hold master’s degrees or Ph.D.’s, but they all have the same relentless drive to ingest new information and ideas.

Communications Skills

Analysis was once seen as the domain of shut-ins and nerds, but the rapid growth of corporate, government, academic and even public interest in the field has put the best communicators are the front. People like Nate Silver, a data analyst who specializes in elections, sports and news, have emerged as public figures who are now invited onto weekly talk programs to discuss the news of the day. Organizations also have many inward-facing analyst positions where the jobs are fundamentally about explaining what all the data means.

If you’re someone who loves to tell others what you’ve learned, there is a place for you in the analysis world. Those who can understand the math and convert it into actionable insights are increasingly in very high demand.

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Big Data

Improving the Effectiveness of Patient Care with Data Analytics

One of the biggest revolutions in the last 10 years in the field of medicine has been the advent of healthcare analytics. Leveraging large data pools, machine learning and new diagnostic technologies, practitioners have been able to find innovative ways to improve patient outcomes. Patient data analytics incorporates information from a wide range of fields, including pharmacology, genomics, personnel management and even biometrics. In addition to monitoring patients, organizations are now able to monitor staff members in order to optimize their availability and work efficiency. Let’s take a look at some of the factors driving this revolution.

Patient Data Analytics

Among the hardest things for doctors to do is to hold the whole dataset regarding a single patient in their heads. Computerized systems allow doctors access to massive databases about their patients, and they also permit them to make use of pattern-recognition technologies to get out in front of problems that even a trained physician might not readily see. For example, a team of researchers has developed a method for determining whether a patient is likely to develop thyroid cancer using a system that achieves greater than 90% accuracy in predicting whether a growth is or will become malignant.

Dashboards also permit doctors to readily access both the data and analysis. Where a practitioner 20 years ago might have to wait for a few pages of patient information to be emailed or faxed to them, they can now pull up a full medical history on their tablet. If the doctor needs to dig deeper, they can see information about medications the patient is on, how the person was diagnosed during previous visits and even the times of the year when the individual has had the most trouble. This can make it much easier to spot patterns that lead to a diagnosis.

Collecting and Analyzing Big Data

While we tend to think of big data as a 21st Century innovation, the reality is that researchers have been collecting the data to do hard science much longer. Famous ongoing data collections efforts like the Framingham Heart Study have been active since the middle of the previous century. What has changed, though, is that we now have patient data analytics packages that can compress massive amounts of data into a useful work product in a matter of minutes, hours or days.

This sort of work at scale can also provide early warnings. CDC data is used to produce daily maps and charts showing the progress of flu season each year. Rather than trying to make a best guess based on rumors and news reports, hospital administrators can figure out when to begin stockpiling for flu season by referring to hard data.

Organizational Improvements

While it’s easy to think the best way to improve patient outcomes is to focus on direct treatment options, there’s also a lot to be said for using healthcare analytics to revolutionize medical facilities as organizations. A good analytics package can help an administration decide:

  • When to ramp up or cut hours
  • How to economize inventories of critical drugs and supplies
  • What forms of continuing education to encourage staff members to pursue
  • How to get patients to heed the advice of doctors and nurses

Except for small clinics and general practices, most healthcare organizations are fairly large operations. They have to deal with the same problems other corporate entities do, such as taking shipments, ordering new materials and scheduling workers. Analytics can even tell a hospital whether the price-to-performance expectation of doing something like adding a new wing will be worthwhile.

Patient Satisfaction

Assessing whether you’re satisfying patients can be a major challenge. Unsatisfied patients may never complain directly to you, instead taking their business to other facilities and expressing their anger on social media. People don’t return questionnaires in great numbers, making it difficult to be proactive even when you strongly want to do so.

Data mining and analytics allow modern organizations to monitor many sources of information regarding both quality control and patient satisfaction. Social media mentions, for example, can be broken down into positive, negative and neutral sentiments. An organization can monitor mentions of its name and the names of its staff members. Machine learning is now so advanced that error correction can be used to identify social media mentions that get the spelling of a doctor’s name wrong.

Information can be compared and contrasted, too. A hospital might compare its analytics regarding staffing against social media sentiment. This can help administrators draw a line between what does and doesn’t drive patient satisfaction. Solutions can then be devised based on what has worked best to make patients happy, and it can all be done without the difficulties associated with satisfaction surveys.

Adopting and applying healthcare analytics, though, demands a cultural change. In particular, an organization must forge a culture of following the data where it leads. If patient satisfaction metrics indicate that a hospital is falling short in key areas, there still has to be a responsive culture present within the administration. With time, however, an organization can develop both the infrastructure and culture required to make better decisions for its patients based on hard data.

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Big Data

Data Analysis Automation: Accelerate Your Digital Transformation

Advances in modern computing have made it possible to analyze more data than ever before. Data analysis automation has the potential to accelerate digital transformations for companies across a wide range of industries. Before you take the first steps toward that future, though, it’s wise to understand what uses cases data analysis automation is best suited for and which ones may present challenges. It’s also a good idea to understand the digital transformation process. Let’s take a look at how your company can benefit from both.

Where Data Analysis Automation Excels

Automating the analysis of data does not provide uniform results. Automation generally works best in situations where data lends itself to many iterations of analysis. This is one of the main reasons that preparing data is a critical part of the process.

If the information going into the system presents problems, the odds of getting useful analysis drop significantly. While many new approaches, such as the use of neural networks to conduct unsupervised machine learning, can iron out some of the uglier bits, providing clean inputs remains the easiest way to not have trouble. This means ensuring that data fields are prepped to be uniform, scrubbing data for things like unreadable characters and performing initial analysis to remove anomalies and outliers.

Highly prepared data is the best sort to use in an automated process. For example, consistently formatted datasets from a trusted government source, such as weather data, tend to require less preprocessing.

The amount of available data also matters. If you’re trying to glean information from a couple of thousand data points, the data set may be too paltry for use. That’s especially the case once data points have been scrubbed from the set. More robust data sets, such as Amazon’s studies of billions of searches for products, also tend to lend themselves better to data analysis automation.

Developing a Culture That Values Data

Digital transformation calls for more than just hiring a few programmers, engineers and data scientists. At its core, the transformation of a company calls for a shift in its culture. This process does not occur overnight, and it requires on-boarding many employees and decision-makers. Hiring practices have to incorporate a data-centric worldview as being just as important as hiring “self-starters” and “team players.”

As painful as it may be to do so, companies also have to confront off-boarding folks who refuse to come along with the transformation. While an honest effort should be made to onboard all personnel, there will come a point where providing severance packages to those who struggle with the change will be necessary.

Choosing a Direction

A company must make an overt effort to pick a direction when it adopts data analysis automation. While it might seem easier to leave this up to the data people, that risks creating a hodgepodge of semi-compatible systems and cultures within your organization. Planning needs to be done to produce documents that outline what the organization’s philosophies are and the kinds of hardware and software it’ll use to accomplish its goals.

There are many major frameworks on the market today. Plenty of them are free and open-source, and those frameworks are often robust enough to do the heavy lifting a company requires. For example, the combination of Tensorflow and Python is wildly popular. These two are often used in conjunction with nVidia’s CUDA Toolkit for GPU acceleration.

Each choice will have its pros and cons. A software stack built on Linux, Python, Tensorflow and CUDA, for example, will call for engineers with specific job skills. Likewise, maintaining and updating software and hardware will require close attention to the requirements of the environment. A new version of CUDA, for example, might open up opportunities for machine vision analysis to be exploited, but it may also call for recompiling existing software and code to operate properly. Diving into such changes willy-nilly can cause the entire software stack to collapse.

Good planning documents should provide fair consideration to the available options. A reasonable comparison might be made between the virtues of OpenCL versus CUDA. There should be a good reason for the final choice you arrive at, such as the relative costs of doing GPU acceleration on nVidida versus AMD hardware.

Compliance

A disconnect between data analysis automation and the real-world things data points represent is a major risk. In an increasingly strict regulatory environment, failures of compliance come with major costs. It’s prudent for an organization to not only think about how it will acquire and process data, but how it will protect the privacy and well-being of people who may be represented by data points. Each company should also consider its own values during the digital transformation process.

Conclusion

The hardest part of automating analysis is ramping up capabilities. Your company will have to plan for a variety of challenges, such as how it will store and archive the data, what will be done with analysis and how it will report its work.

Analysis, though, lends itself to many business cases. Once your company is generating usable work products, you can also begin to develop new business models. For some companies, the focus will be on improving efficiency and processes. Others will discover that selling analysis to other parties is a viable business model in its own right. In time, you will find the returns from building out automated analytics capabilities tend to compound very quickly once you get rolling.

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Artificial Intelligence Big Data

A Data Monetization Strategy: It’s What Your Business Needs

Collecting and monetizing data is a goal that many organizations now have. Setting out a goal, however, isn’t the same as actually employing data monetization strategies.

When we think about data monetization strategies, they can be broadly put into two main camps. These are strategies that are meant to be:

  • Cost-saving measures
  • Revenue-generating ones

Similarly, strategies tend to be either internally or externally facing. Let’s take a look at how each approach works and how they might fit your operation’s needs.

Cost-Saving Data Monetization Strategies

Oftentimes, the simplest data monetization strategy is the one that leverages something a company already has. For example, a large home nursing agency has lots of data about the appointments it makes. That data can be leveraged to make determinations about when to schedule employees, how to handle travel and even what order clients should be visited in.

A cost-saving data monetization strategy is typically internally facing. This is because the easiest data to get your hands on is what your company has.

There are, however, businesses that have emerged that provide cost-saving data monetization strategies to others. Many consulting firms now help other companies make the most of their existing data in order to:

  • Improve processes
  • Spot fraud
  • Create better information-driven products
  • Develop better compliance measures
  • Anchor analysis

If a problem can be identified, there’s a good chance it can be monetized if you can find and exploit a data pool related to it.

Revenue-Generating Data Monetization Strategies

Streamlining a business is one thing, but at the end of the day, your organization needs to turn a profit. The best way to accomplish that sometimes is to employ a revenue-generating data monetization strategy.

A simple version of this approach is assembling collected data into products that can be sold to other parties. This is an especially good plan if you’re trying to monetize what is fundamentally dead data. For example, a healthcare company might not have much use for decades-old epidemiological data. Plenty of researchers, though, would pay to get their hands on that data. Bear in mind, however, that anonymization is often necessary when selling data products.

Turning information into a new product is another way to generate revenue. Your company might compile loads of data on trends in your industry, for example. Converting such information into reports that are sold to outside parties is one of the most time-honored data monetization strategies.

Creating whole new opportunities is another approach. A company might focus on culling existing data to determine where there are new markets to enter. For example, your organization might examine international sales and see that loads of folks in Australia are ordering your products online. This may suggest an opportunity to open up new stores in the country.

Inward vs. Outward

Another question is just how inward- or outward-facing you want your data monetization strategies to be. Naturally, some organizations are reluctant to put their data in places where competitors might take advantage of it. There also may be concerns about regulations limiting the transfer of data.

In some cases, an outward trajectory is the only viable approach. In the previous example involving dead data, there simply may be no other way to extract any more value from the data as a product.

The question of inward versus outward monetization sometimes hinges on building a business model. Inward-facing models generally are more sustainable because they don’t depend on outside parties continuing to pay for reports or subscriptions. Conversely, an inward-facing model frequently has an installed limit on how much value it can generate because its audience is capped.

Preparing a Data Monetization Strategy

Having a strategy isn’t enough. To put one to work, you need to prepare the data itself and to be prepared for several potential issues.

If your operation doesn’t already have a large data infrastructure and a data-centric culture, you’ll need to put that in place. This entails:

  • Developing a business case for the strategy
  • Establishing processes and a compliance structure
  • Hiring professionals who can handle data
  • Building up servers and networks for storage and processing
  • Refining processes
  • Maintaining the strategy long-term

In many instances, the strategy you choose will guide the decision-making process. For example, a company that’s building a model for selling anonymized customer data to third parties will need to build its processes around privacy and consent concerns. A solution will need to be in place for customers to deny the use of their information, and an audit system will need to be designed to ensure privacy concerns are addressed.

Data monetization is a huge opportunity, especially for organizations that are already accumulating loads of information. It does require a cultural commitment, though, to handling the data itself and treating it with an appropriate level of care. In time, your company can realize major savings and turn its data into a profit generator.

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BI Best Practices Big Data Business Intelligence

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

computer

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.

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Categories
Artificial Intelligence Big Data

Leveraging the Power of AI Can Drastically Fuel the Manufacturing Industry

AI-enabled machines are creating an easier path to the future of manufacturing by yielding a pool of advantages, including providing new opportunities, improving production efficiencies, bringing machine interaction closer to human interaction, etc.

According to the market’s facts and statistics, the global marketplace for artificial intelligence in manufacturing will be predicted to reach $15,237.7 million by 2025. The market was valued at $513.6 million within 2017 and is projected to rise at a CAGR of 55.2%.

AI is Essential for the Next-Gen Software in the Manufacturing Industry

Since AI came into view over the last few years, it has managed to surmount an array of internal challenges that have haunted the manufacturing industry for decades. This ranges from the lack of expertise to complexity in decision making, issues related to integration, and overloaded information. With AI, manufacturers can completely transform their proceedings.

Unlike healthcare, finance, utilities, and e-commerce industry, AI-powered analytics along with real-time insights have already rolled out in the manufacturing field to assist businesses in upsurging their revenues and market shares faster than their competitors.

In a 2018, Manufacturer’s Annual Manufacturing report revealed 92% of senior manufacturing executives say that ‘Smart Factory’ digital technologies, which includes AI, will provide them access to escalate their productivity levels and enable staff to work “smarter”. In the same way, Forrester, a global research firm, emphasizes that 58% of business and technology professionals are exploring AI systems, while just 12% are actively using them.

AI Growth in Manufacturing

Advancements in manufacturing automation and the increase in demand for big data integration are fueling the AI growth in manufacturing market. Additionally, extensive utilization of machine vision cameras in manufacturing applications – such as machinery inspection, material movement, field service, and quality control – could also accelerate the AI growth inside manufacturing.

Leveraging AI within manufacturing plants may allow businesses to entirely transform their own proceedings, assisting the industry in accomplishing directed software, 24/7 production, reduced operational costs, safer operational environment, new opportunities for employees, etc. Additionally, bringing AI into the production industry would necessitate a huge capital investment that can significantly increase the return associated with investments. While intelligent machines start taking care of routine activities, manufacturers can enjoy significantly lower operating costs.

AI further enables machines to reap and extract data, recognize patterns, learn and adapt in order to new things or situations through device intelligence, and speech recognition.

Manufacturers, by utilizing AI, will have access to…

  • Produce data determined decisions swiftly
  • Facilitate improved production outcomes
  • Advance process effectiveness
  • Decrease operational costs
  • Improve scalability and product development

AI Trends in Manufacturing Businesses

Within the manufacturing industry, AI could be integrated with the particular Internet of Things (IoT) to deliver supplies and services in order to customers. IoT can also convey in-depth measurements back to manufacturers and distributors to analyze quality and factors that might impel fiascos.

According to a research report, AI technologies are approaching the increase of production by 40% or more through 2035. Also, this technology will fortify the economic growth by an average of 1.7% across 16 industries by 2035.

Let’s take a look at some AI trend examples…

Two-years back in October 2017, computer software giant Oracle launched new AI-driven apps for supply chain, manufacturing, and other professionals. Last year, IBM released an AI-optimized Watson Assistant for businesses, which usually is a smart enterprise assistant powered with AI features.

In brief, adoption of AI can assist in empowering manufacturers to effectively deploy predictive and preventive maintenance, flexible automation, automated quality control and demand-driven production.

 

Categories
Big Data Data Analytics

Prescriptive Analytics: The Ultimate Purpose of Your Data

  • Descriptive
  • Predictive
  • Prescriptive

When we think about answering the question of what an organization should do, that brings us into the domain of prescriptive analytics. Let’s take a look at what the world of prescriptive analytics is all about and how it can benefit your operation.

Prescriptive Analytics Compared to Other Methods

One way to understand what a prescriptive approach actually involves comparing it to other forms of business analytics. Descriptive analytics helps us get a handle on what a problem looks like now and what it looked like in the past. In particular, it generally does not attempt to address questions about causal relationships. The goal, instead, is simply to lay the bag of snakes out straight.

For example, historical economic analysis is a form of descriptive analytics. An economist looking back at data regarding the Tulip Mania during the 1600s probably isn’t trying to create a model for how bubbles form in the modern economy.

The world of predictive analytics is at the opposite end of the scale. Researchers there are trying to examine current data and trends in order to determine where things will land in the future. For example, a report on the global impact of climate change might be intended to just figure out what the heck is on the horizon.

Prescriptive Analytics

Prescriptive analytics cuts to the core of three questions:

  • What can we do?
  • What should we do?
  • What might others do?

The oil and gas industries are big spenders on prescriptive analytics. Exploring regions for oil, for example, opens up questions that go well beyond what descriptive or predictive analytics can do. An oil company does need to take a descriptive view of a deposit, and it does need to predict things like global demand and supply trends. When drilling a well, an oil company has to prescribe solutions to problems like:

  • Boring through rock
  • Fluid and gas pressures in deposits
  • Where to position rigs
  • How many workers to assign to projects

These kinds of business analytics are meant to assess risks, exploit opportunities and maximize returns. A state government might, for example, want to know at what grade level it should spend the most money to ensure that economically disadvantaged kids can get ahead. To do this, they have to figure out where the risks to those kids arise and what opportunities aren’t being presently exploited.

Conclusion

For many organizations, prescriptive analytics projects represent their goals. Decision-makers are empowered to take action when prescriptions are grounded in hard data. Rather than producing tons of data that just goes into spreadsheets and databases never to be read, organizations can convert massive amounts of information into answers to pressing questions.

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Categories
Big Data Data Analytics

Data Quality: Making Change a Choice

In the modern world, nothing stays the same for long. We live in a state of constant change; new technologies, new trends and new risks. Yet it’s a commonly held belief that people don’t like change. Which led me to wonder, why do we persist in calling modify management initiatives “change management” if people don’t like alter.

In my experience, I have not found this maxim to be true. Actually, nobody minds change, we evolve plus adapt naturally but what we do not really like is being forced to change. As such, when we make a choice to change, it is often easy, fast and permanent.

To put that into context, change is an external force imposed upon you. For example , if I tell you I want you to change your attitude, you are expected to adapt your patterns of behaviour to comply with my idea associated with your ‘new and improved attitude’. This is difficult to maintain and conflicts with your innate human need to exercise your own free-will. However, if I ask a person to choose your own attitude, this places you in control of your personal patterns of behaviour. You can assess the particular situation and decide the appropriate attitude you will adopt. This…

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Categories
Big Data Data Quality

What is Data Cleaning?

What is Data Cleaning?

Lets face it, most data you’ll encounter is going to be dirty. Dirty data yields inaccurate results, and is worthless for analysis until it’s cleaned up. Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. There are several methods for data cleansing depending on how it is stored along with the answers being sought.

Data cleansing is not simply about erasing information to make space for new data, but rather finding a way to maximize a data set’s accuracy without necessarily deleting information.

Data cleansing is the process of spotting and correcting inaccurate data. Organizations rely on data for many things, but few actively address data quality. Whether it’s the integrity of customer addresses or ensuring invoice accuracy. Ensuring effective and reliable use of data can increase the intrinsic value of the brand.  Business enterprises must assign importance to data quality.

Since there are many different types of data quality issues, each one requires different tactics to clean them.  

For one, data cleansing includes more actions than removing data, such as fixing spelling and syntax errors, standardizing data sets, and correcting mistakes such as missing codes, empty fields, and identifying duplicate records. Data cleaning is considered a foundational element of data science basics, as it plays an important role in the analytical process and uncovering reliable answers.

Most importantly, the goal of data cleansing is to create datasets that are standardized and uniform to allow business intelligence and data analytics tools to easily access and find the right data for each query.

Why Clean Data?

A data driven marketing survey conducted by Tetra data found that 40% of marketers do not use data to its full effect. Managing and ensuring that the data is clean can provide significant business value.

Improving data quality through data cleaning can eliminate problems like expensive processing errors, manual troubleshooting, and incorrect invoices. Data quality is also a way of life because important data like customer information is always changing and evolving.

Business enterprises can achieve a wide range of benefits by cleansing data and managing quality which can lead to lowering operational costs and maximizing profits.

How To Clean Data

To be considered high-quality, data needs to pass a set of quality criteria. Those include:

  • Valid: The degree to which the measures conform to defined business rules or constraints. When modern database technology is used to design data-capture systems, validity is fairly easy to ensure: invalid data arises mainly in legacy contexts (where constraints were not implemented in software) or where inappropriate data-capture technology was used (e.g., spreadsheets, where it is very hard to limit what a user chooses to enter into a cell, if cell validation is not used). Data constraints fall into the following categories.
    • Data-Type Constraints – e.g., values in a particular column must be of a particular datatype, e.g., Boolean, numeric (integer or real), date, etc.
    • Range Constraints: typically, numbers or dates should fall within a certain range. That is, they have minimum and/or maximum permissible values.
    • Mandatory Constraints: Certain columns cannot be empty.
    • Unique Constraints: A field, or a combination of fields, must be unique across a dataset. For example, no two persons can have the same social security number.
    • Set-Membership constraints: The values for a column come from a set of discrete values or codes. For example, a person’s gender may be Female, Male or Unknown (not recorded).
    • Foreign-key constraints: This is the more general case of set membership. The set of values in a column is defined in a column of another table that contains unique values. For example, in a US taxpayer database, the “state” column is required to belong to one of the US’s defined states or territories: the set of permissible states/territories is recorded in a separate States table. The term foreign key is borrowed from relational database terminology.
    • Regular expression patterns: Occasionally, text fields will have to be validated this way. For example, phone numbers may be required to have the pattern (999) 999-9999.
    • Cross-field validation: Certain conditions that utilize multiple fields must hold. For example, in laboratory medicine, the sum of the components of the differential white blood cell count must be equal to 100 (since they are all percentages). In a hospital database, a patient’s date of discharge from hospital cannot be earlier than the date of admission.
    • Data cleaning to correct validity issues can often be done programmatically.
  • Accuracy: Quite simply, is the data right. The conformity of a measure to a standard or a true value. Accuracy is very hard to achieve through data cleaning in the general case, because it requires accessing an external source of data that contains the true value: such “gold standard” data is often unavailable. Accuracy has been achieved in some data cleansing contexts, notably customer contact data, by using external databases that match up zip codes to geographical locations (city and state), and also help verify that street addresses within these zip codes actually exist.
  • Completeness: The degree to which all required measures are known. Incompleteness is almost impossible to fix with data cleaning methodology: one cannot infer facts that were not captured when the data in question was initially recorded. In the case of systems that insist certain columns should not be empty, one may work around the problem by designating a value that indicates “unknown” or “missing”, but supplying of default values does not imply that the data has been made complete.
  • Consistency: The degree to which a set of measures are equivalent in across systems. Inconsistency occurs when two data items in the data set contradict each other: e.g., a customer is recorded in two different systems as having two different current addresses, and only one of them can be correct. Fixing inconsistency is not always possible: it requires a variety of strategies – e.g., deciding which data were recorded more recently, which data source is likely to be most reliable (the latter knowledge may be specific to a given organization), or simply trying to find the truth by testing both data items (e.g., calling up the customer).
  • Uniformity: The degree to which a set data measures are specified using the same units of measure in all systems. It is often quite easy to ensure this through data cleaning early in the process, but as the process moves along, and data is transformed and changed, it becomes far more difficult. In datasets pooled from different locales, weight may be recorded either in pounds or kilos, and must be converted to a single measure using an arithmetic transformation. This also points out the problem of naked metrics, where values like weight may be recorded as an integer value. Unless there is another column directly next to it, or a notation in the column heading, it can be next to impossible to determine whether a value is in kilos or pounds, or celsius or Fahrenheit.

The term integrity encompasses accuracy, consistency and some aspects of validation but is rarely used by itself in data-cleansing contexts because it is insufficiently specific.

How Can I Use Data Cleaning?

Regardless of the type of analysis or data visualizations you need, data cleansing is a vital step to ensure that the answers you generate are accurate. When collecting data from several streams and with manual input from users, information can carry mistakes, be incorrectly inputted, or have gaps.

Data cleaning helps ensure that information always matches the correct fields while making it easier for business intelligence tools to interact with data sets to find information more efficiently. One of the most common data cleaning examples is its application in data warehouses.

After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data

A successful data warehouse stores a variety of data from disparate sources and optimizes it for analysis before any modeling is done. To do so, warehouse applications must parse through millions of incoming data points to make sure they’re accurate before they can be slotted into the right database, table, or other structure.

Organizations that collect data directly from consumers filling in surveys, questionnaires, and forms also use data cleaning extensively. In their cases, this includes checking that data was entered in the correct field, that it doesn’t feature invalid characters, and that there are no gaps in the information provided.

More on Data Cleaning:

https://en.wikipedia.org/wiki/Data_cleansing

https://searchdatamanagement.techtarget.com/definition/data-scrubbing

Categories
Artificial Intelligence Big Data Data Analytics

Anomaly Detection — Another Challenge for Artificial Intelligence

It is true that the Industrial Internet of Things will change the world someday. So far, it is the abundance of data that makes the world spin faster. Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the particular pool of collected data, has become one of the main objectives of the Industrial IoT.

Abnormality detection refers in order to the identification associated with items or occasions that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by the human expert. Such anomalies can generally be translated into problems such as structural defects, errors or frauds.

Examples of potential anomalies

A leaking connection pipe that leads to the shutting down of the entire production line;
Multiple failed login attempts indicating the possibility of fishy cyber activity;
Fraud detection in financial transactions.

Why is it important?

Modern businesses are beginning to understand the particular importance of interconnected operations to get the full picture of their…

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