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Author: Scottie Todd

Digital Marketing Lead

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Big Data Business Intelligence Data Analytics Data Preparation

How to Cure Your Company’s Spreadsheet Addiction

The use of spreadsheets in business today is essential for the vast majority of people. From quick calculations and generating reports to basic data entry, spreadsheets have a way of working themselves into our daily tasks. What’s not to love about spreadsheets? They’re quick, easy to use, require little to no training, and are quite powerful tools overall. However, developing too much of a dependency on them can be problematic, especially when used as a workaround for other solutions due to convenience.

This dependency problem is commonly referred to as spreadsheet addiction. While referring to this phenomenon as an addiction might seem a bit extreme, many organizations find themselves heavily reliant on the use of individual spreadsheets to perform core functions. This high usage rate causes many problems and can ultimately hinder a company’s growth. Let’s explore the potential causes of this addiction as well as review possible treatment plans of action.

What’s Wrong with Using Spreadsheets?

While Excel and Google Sheets can be quite effective in their own right, heavy reliance on spreadsheets can create risk and cause a number of negative effects. 

A few examples of potential problems they create are:

  • Things Start to Break – As the size of the dataset increases, things within your spreadsheet inevitably start to break. Once this starts to occur, it can be seemingly impossible to identify the source of the problem. You’ll likely drain more time and resources into finding and fixing the issue than on your actual analysis. These breaking points also create the risk for errors and other data corruption.
  • Static and Outdated Information – Exporting data from your CRM or ERP system instantly causes the information to become outdated. Spreadsheets don’t allow you to work with your data in real-time, additionally, it’s also extremely difficult to implement any form of automation within your sheets. This creates more work for users as well as poses the problem of inaccuracy.
  • Impedes Decision Making – Spreadsheets are notoriously riddled with errors, which can be costly when it comes to decision making. You wouldn’t want to base any kind of decision on a forecast that is more likely to be inaccurate than not. Reducing discrepancies, specifically human error will improve decision making overall.

Treatment Method to Spreadsheet Addiction

Regardless of the severity of your company’s spreadsheet dependency, effective treatment is no small feat. Change doesn’t happen overnight and you should approach your treatment plan as an iterative process. While this list is not exhaustive, here are a few pillars to consider when promoting change.

Evaluation of Symptoms

First, you must identify how spreadsheets are currently being used. It’s important to start with a comprehensive overview of your situation in order to form an effective plan of action. 

To access your addiction level, start by asking yourself questions such as:

  • How are spreadsheets used by an individual user and across departments?
  • What company processes involve the use of spreadsheets?
  • How are spreadsheets used as a method of collaboration?

Drill down how spreadsheets are being used from the company level down to the individual users. Determine not only how they are being used by departments but also their frequency, purpose, and role in daily operations.

Assign Ownership

Next, assign an individual to lead or build a small team to take ownership of the project. If no one feels they are directly responsible for driving change, the bystander effect will inevitably rear its ugly head on the situation. Assigning responsibility for the transition away from spreadsheets will facilitate the flow of change.

New Solutions and Systems

A new solution requires a widespread change to business processes and will ultimately impact day to day operations. This is the main reason spreadsheet addiction is prolonged in businesses, everyone is more comfortable with their usual way of doing things. But implementing the proper tools and systems is vital to decreasing dependency on spreadsheets. 

Use your evaluation to identify your company’s business intelligence needs and acquire tools accordingly. While this transition might require an initial upfront cost, investing in the proper data and analytics tools will help reduce costs in terms of efficiency and labor in the long run.

Promote User Buy-In 

Buy-in from employees across all levels is crucial to the acceptance of new solutions and business processes. Spreadsheet addiction will prevail if users aren’t comfortable with using the new systems put into place. Learning is required when it comes to any change, it’s essential to offer training and available support resources to aid the shift.

In the end, accept that there will always be some tasks and projects done through Excel or Google Sheets. The important thing is that not everything or even the majority of work will be done through these platforms. Though beating spreadsheet addiction might come with some withdrawals, driving change now will foster greater efficiency in the long run. 

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Big Data Data Preparation Data Visualization

3 Useful Excel Tricks You Wish You Knew Yesterday

Microsoft Excel has become an essential staple for workplace professionals across every industry, especially when quickly working with data and performing basic analyses. With hundreds of functions, it can be overwhelming to try to learn them all as well as know which are most effective. But if used correctly, these functions can help save you an immense amount of time and headache. Let’s explore a few classic Excel tricks every analyst should have in their toolbox.

1. Heatmap

It’s easy to get lost in the hundreds of rows and columns that make up most spreadsheets. You might be left asking yourself, what do all of these numbers mean? This is where data visualizations are key in helping you understand your data and generate insights quickly. 

One effective way to do this is with the use of color and Excel’s heatmap function. To put it simply, heat maps are a visual representation of your data through the use of color. These charts are perfect for comprehensive overviews of your data as well as additional analysis.

This trick can be broken down into three simple steps:

  1. Select the cells and values you want to include. 
  2. Under the Home tab, click on Conditional Formatting. 
  3. Select Color Scales from the drop-down menu and choose your desired color scale selection.

Following these steps, you can now see your data highlighted in a gradient-based on its value. This can visually assist, for example, in identifying critical dips in sales or inventory by highlighting those cells as red. Overall, heat maps are extremely versatile and can be used to understand data intuitively. They also make for a great visual stimulus in any dashboard or report!

2. Remove Duplicates

The last thing you want in your data is duplicate entries or values. This poses an issue of inaccuracy and other inherent risks in your analysis. Though, removing these duplicates is quite simple when using Excel, here are the steps to follow for one method.

To begin, we need to first identify if there are any duplicates present in your spreadsheet. We can do this by highlighting any duplicates through Excel’s Conditional Formatting function. 

  1. Under the Home tab, click on Conditional Formatting.
  2. Select Highlight Cells Rules from the drop-down menu, then select Duplicate Values.
  3. Determine your formatting preferences and click OK.

Any duplicates present in your data will be highlighted based on the color preferences you determined earlier. Now that you’ve detected the duplicates, you can easily remove them by going to the Data tab and clicking Remove Duplicates. Excel will then tell you how many duplicates were detected and the total removed from your sheet. Duplicate free in only a few simple clicks! This trick can help you minimize discrepancies as well as save time trying to manually detect and delete duplicate values.

3. Filling All Empty Cells 

Chances are your dataset contains a few empty cells, this could be due to incomplete data or any number of reasons. In order to avoid any issues when it comes to your analysis or when creating models, it’s important to fill these cells ahead of time.

Follow these steps to identify and fill all empty cells at once:

  1. Under the Home tab, click Find & Select.
  2. Select Go To Special from the drop-down menu and select Blanks from the provided menu options.
  3. Fill an empty cell with your desired value or text (e.g. N/A) and press CTRL + ENTER.

With this function, all of your empty cells can be identified and filled in a matter of seconds. This trick will help you save time ciphering through columns trying to manually detect and fill empty cells. Additionally, this can be especially helpful when working with large data sets used for creating models.

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Big Data

Top 5 Strategies to Avoid Analysis Paralysis

While you might think pouring more and more time into your analysis is the most constructive approach to producing a quality outcome, sometimes exhausting your analysis can lead to creating more questions than answers. Analysts can often experience anxiety and indecision in their work due to the abundance of data and information available to them.

In the field of data analytics, this is referred to as analysis paralysis, a common issue that can lead to significant problems when it comes to productivity and decision making. Let’s take a closer look at analysis paralysis, identify its potential implications, and review steps on how to avoid it.

Why You Should Avoid Analysis Paralysis

Though the term sounds quite ominous on its own, you might not consider overthinking your data to be an issue at the forefront of concern. Nevertheless, there are many additional reasons to avoid analysis paralysis beyond the obvious damper to productivity.

Overthinking and overanalyzing the data to drive a decision can cause a number of problems such as:

  • Slower and prolonged decision-making processes
  • Draining of time and resources 
  • Increased mental fatigue
  • Lowered creativity and performance

How to Avoid Analysis Paralysis

While every case is different in its own right, there are tactics to combat being paralyzed by your data. Here are a few strategies to help you avoid these problems and keep you moving forward in your report.

Accept that Uncertainty is Inevitable

If you’re searching for the perfect answer or the perfect data model, chances are you won’t be able to find it. While you might want to exhaust your data from every angle, it’s important to accept that there will always be deviations and random occurrences outside of your control. You should still be thorough in your efforts, but remember that uncertainty is inevitable in any analysis.

Ask yourself if you have enough information to explain and promote discussion amongst executives and those involved. If the answer is yes, then you probably have enough value from your analysis to conclude your efforts.

Better Visibility

Using your data to drive a decision becomes complex when all of your data isn’t combined in one unified source. If each department houses their data in separate systems and acts as their own entity, there is an increased chance your analysts are missing key pieces of information.

This is where your analytics tools come into play, easy access to real-time data visualizations and predictive analytics will increase your analysts’ overall visibility. Better access to company-wide data and interactive tools allow for a more comprehensive analysis.

Embrace the Iterative Process

Since simulating decisions is often unmanageable, even with the help of data and predictive analytics, it’s vital to take an iterative approach. Like many things in business, data-driven decision making is an ongoing process. Any individual project or decision derived from your analysis is not likely to be the end-all conclusion on the matter. Embrace the iterative process and take a learning approach towards all of your results, which will help your organization in the long run.

Weigh the Costs

Think about what you’ve learned thus far and evaluate if there are any key gaps in accessing your original assumptions. How much is it costing you to continue your efforts in comparison to how much value is being created? Placing a value on the amount of time dedicated to further queries will enable you to put the rate of additional analysis into perspective. This will help you to determine from a basic cost-benefit standpoint whether or not to proceed. Though it might be difficult to accurately quantify the value of additional gains, it’s good to get a general feel for when your analysis isn’t producing enough incremental value.

Always Keep Your Objectives in Mind

The primary motivations behind the decision or desired outcome should always be kept in mind. Overarching business objectives for the project should serve as your foundational guide for evaluating assumptions. Focus on assessing the validity of your initial hypothesis when it comes to deriving final conclusions. Focusing on objectives will also allow you to cipher through greater amounts of information without feeling overwhelmed.

Overall, with the endless amounts of data being created today, it’s easy to get lost in the numbers. There will always be additional factors to consider and room for further analysis in any project. However, it’s important to identify the point of diminishing returns when it comes to investing your time in delving deeper into the data. 

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Data Analytics Data Science Careers

The Secrets to Building Highly Effective Data Science Teams

The use of data science has become increasingly essential to success in today’s business climate. As more teams and positions are being created in the field of analytics, leaders have found establishing successful data science teams to be a difficult task to manage. Let’s explore the key elements to building a highly effective data science team for your next project as well as how to maximize your current talent.

Promoting Curiosity is Key

Curiosity is at the root of any successful team or project. Data can help organizations uncover and understand almost every aspect of their business, from key marketing drivers to even forecasting seasonal changes in sales. But first, your analytics team has to be willing to continually ask the data questions and perform deep dives into the information. While data visualizations provide comprehensive value when it comes to your data, not every issue or insight can be seen from the surface. 

Additionally, curiosity and exploration are important in fostering engagement amongst analysts. Increased engagement can lead to greater involvement in projects and higher quality insights that may not have been discovered otherwise. Implementing a weekly brainstorming discussion or providing access to educational courses can assist in stimulating curiosity when it comes to how your team approaches company data.

Make Experimentation and Research a Priority

Time management is another fundamental element to team effectiveness, what you spend your time on is vital to efficiency. It’s important to assess where time could be better spent and identify any possible inhibitors or distractions.

To evaluate how your team’s time is being allocated, start by asking questions such as: 

  • What type of projects do you typically spend the majority of your time on?
  • How often do you spend time on research or long-term projects?
  • Do non-technical users in your organization have easy access to data or do they require assistance from a data scientist?

The answers will illuminate where your team’s focus lies in the day-to-day. Your team shouldn’t be constantly tasked with requests for administering access to data or answering basic ad-hoc questions. These types of tasks can easily be supported by improved infrastructure or self-service analytics tools. While your data science team might be highly proficient in these assignments, an abundance of these requests will ultimately distract your team from their data science research and higher-level work. You should make a point to limit these requests in order to emphasize research and experimentation. 

High-Level Goals are Known and Understood

The team needs to understand the end game of any project and how it fits into the organization’s overall goals. It’s not uncommon for technical teams to experience a disconnect from business teams and their objectives. Making sure these business targets are known and understood will allow your team to better communicate their findings in relation to company goals. 

Furthermore, you should clearly define how your data science team should be interacting with other areas of the organization. Team members should regularly be involved in discussions surrounding strategies to stay up to date on the key drivers behind projects across departments. This will ensure effective collaboration between your data science team and any business stakeholders. 

Feedback and Continuous Improvement

There is always room for improvement, continuous learning is fundamental to the long-term success of any team. Be sure to carve out time at the end of a project to review performance. You should not only highlight positives and team contributions but also evaluate processes or methods that could be improved. Routine feedback will assure the success of future projects and give those involved an opportunity to progress as a group. 

Overall, there are ultimately many factors beyond this list that contribute to building a highly effective data science team. While every team is unique, providing a foundation for alignment with the business side of the organization, good communication, and exploratory research is key to success. 

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Big Data Business Intelligence Data Analytics Data Preparation

Why Data Warehouse Projects Fail

As organizations move towards becoming more data-driven, the use of data warehouses has become increasingly prevalent. While this transition requires companies to invest immense amounts of time and money, many projects continue to fail. Let’s take a look at the most common reasons why data warehouse projects fail and how you can avoid them. 

There’s No Clear Big Picture

In most cases, these projects don’t fail due to technical challenges. While there might be some obstacles when it comes to loading and connecting data, the leading pitfalls of project failure are predominantly organizational. Stakeholders commonly feel that there is a lack of clarity surrounding the warehouses’ goals and primary objectives.

Companies often see this most prevalently in the division between technical teams and the ultimate end user. You don’t want your architect or engineers to be on a different page than your analysts. Therefore, it’s important to establish the high-level goals behind why you are undertaking this project to all members of your team before putting processes into place. 

Before beginning, the team should have definitive answers to questions like:

  • What are our data goals?
  • What insights are we looking for to satisfy our business needs?
  • What types of questions do we need the data to answer?

Developing a clear understanding of the big picture early on will help you avoid uncertainty around strategy, resource selection, and designing processes. Knowing the company’s “why” behind taking on the initiative will also allow those involved to recognize the purpose of their efforts.

The Heavy Load of Actually Loading the Data 

Despite the organizational obstacles, there are also many hurdles on the technical side of things. Before data can be loaded into the warehouse, it has to be prepped and properly cleaned. This poses an initial challenge as cleaning data is notoriously a time-consuming task. IT leaders are often frustrated by the wasted hours spent preparing data to be loaded.

The primary main concern is the ability of organizations to easily move and integrate their data. Movement and ease of access to data are crucial in order to generate any kind of insights or business value. According to a recent study conducted by Vanson Bourne and SnapLogic, 88% of IT decision-makers experience problems when it comes to loading data into their data warehouse. 

The most common data loading inhibitors were found to be:

  1. Legacy Systems – Migrating data from legacy technology can be time-consuming. However, the primary issue here is that these systems can be difficult to access, making any kind of data movement restrictive.
  2. Unstructured and Semi-Structured Data – Complex data types are tough to manage in any situation. Inconsistencies surrounding structure and formatting drains time and technical resources, preventing effective loading.
  3. Data Siloed in Different Infrastructures – Disconnection of data sources prevents integration across the organization. Many companies have hundreds of separate data sources as they continually grow across departments and with the addition of various projects. 
  4. Resistance to Sharing Data Across Departments – Oftentimes departments act as their own separate entities and aren’t willing to share. The sales team may not want finance to have access to their customer data due to misaligned goals. 

All of these warehouse factors drain an organization’s time and resources, contributing to a lengthier and more costly project overall. Additionally, improperly loading data can cause a number of problems in itself such as errors and data duplication.

Low End User Acceptance

So you’ve successfully moved your data into the warehouse, now what? Another issue that commonly contributes to the failure of data warehouse projects is end user acceptance. As much as new technologies can be exciting, people are inevitably creatures of habit and might not always delve into acceptance. This is where education and training come into play. Onboarding users is vital to the success of any project. 

Establishing a data-driven culture is the first step to promoting user acceptance and engagement. End users should be encouraged to indulge in their data curiosities. Implementing a form of self-service analytics will increase the ease of use for non-technical users and help them quickly gain access to information. These transitional efforts will not only help with the success and use of your data warehouse but also drive better decision making throughout the organization in the long run.

Conclusion

Overall, there are a variety of reasons that contribute to the failure of data warehouse projects. Whether those pitfalls are organizational or on the technical side of things, there are proven ways to properly address them in order to maximize investment and foster successful insights. 

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Big Data Business Intelligence

Don’t Download Your Data to an Excel Spreadsheet

As concerns surrounding data breaches rise amongst IT leaders, security is at the forefront of every company’s operations. Due to the high cost that comes with these breaches, many businesses spend millions to strengthen their defenses. 

Despite all of the human capital and monetary resources dedicated to protecting data, though, many basic data security risks are commonly overlooked. One of the biggest being downloading data to spreadsheets and Excel files. Let’s take a look at the problems behind this security risk as well as its potential impacts.

What’s the Problem?

Downloading your data to an Excel spreadsheet seems simple enough, chances are you’ve done it yourself on multiple occasions. You might have been quickly searching for answers about last quarter’s sales or current inventory levels. Whatever the case, downloading data, particularly to a personal computer, can cause a number of problems.

The task might seem trivial or insignificant in comparison to other security threats, but what are the real implications of downloading data to a spreadsheet?

Data downloaded to a spreadsheet results in problems such as:

  • The inability to monitor or control how the data is used or shared
  • Files become subject to misuse and exploitation
  • An increased risk of hacking and exposure of confidential information 

Non-Security Issues

Beyond the increased exposure to data breaches, there are many other implications. To begin, you’re unable to work with the data in real-time. Data is constantly changing and once the data is removed from the warehouse, it instantly becomes outdated. You don’t want to be making important decisions based on data from last week if the data is shifting every hour. Working with only a mere snapshot in time could create the wrong picture as well as issues of inaccuracy.

What are the Real Costs?

While the potential risk factors might be evident, let’s review what some of the quantifiable costs are. According to a recent study by IBM, the average cost of a data breach in 2020 is approximately $3.86 million, this number rising significantly each year.

Companies are no stranger to these high costs, many have experienced their own security breaches as a result of spreadsheets. In organizations with hundreds or even thousands of employees, human error is inevitable. In 2014, for example, an employee at Willis North America accidentally sent a spreadsheet containing private information to 4,830 employees enrolled in the company’s medical rewards plan. The attachment contained employees’ names, birthdates, Social Security numbers, employee ID numbers, and additional confidential data. 

The insurance broker had to pay for identity theft protection services for all affected employees as a result, costing them thousands. Additionally, the company received a citation from the US Health Insurance Portability and Accountability Act (HIPAA). Costs, though, extend beyond fines and additional losses, the company’s reputation is also an expense to keep in mind. 

Keep it in the Warehouse

While it’s crucial to take the necessary precautions when it comes to data security, all of those efforts could be undermined by something as simple as a spreadsheet. Data is vulnerable when it comes to movement outside of your data warehouse. Making efforts to minimize this risk is key to preventing data breaches, or else it could cost you. 

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Big Data Business Intelligence

5 Essential Steps to Transform Data into Decisions

Working with all of the data in the world provides no value if the insights gained aren’t used to drive decision-making. If you’re interested in building a more data-centric culture within your organization, follow these 5 steps when transforming your data into decisions.

Figure Out What Data Must Be Produced

Every business has specific questions that need to be answered to grow and improve performance. For example, a business might be experiencing high levels of customer churn because its products aren’t connecting with their current audience. Using available data, the company’s analysts may determine, for example, that the churn is occurring because they are targeting the wrong age group or geographic region. 

In this scenario, the big decision that needs to be made is how to target buyers who will become long-term customers. Making that decision, however, starts with figuring out what data is needed. In this case, an analysis of customer churn will ultimately drive decision-making.

Identifying Potential Data Sources

The raw materials for a project come from the data sets you have access to. If you don’t have the necessary data, processes should first be put in place to collect it. In the previous example, the company might want to acquire data by:

  • Reviewing marketing data
  • Collecting information from sales reports
  • Asking customers to conduct surveys
  • Studying customer service interactions
  • Looking at social media posts

How to Properly Target Your Analysis

Especially with a problem such as customer churn, it’s important to figure out what the sentiments toward the products are. There’s a gap between well-targeted buyers who end up frustrated due to issues with customer service, for example, and buyers who made a one-time purchase because there was a killer discount or seasonal trend. 

Detailed sentiment analysis from multiple data sources can shed light on which groups most of your customers fall into. You might find that the previously targeted customers fell into 5 different categories, and a majority of the churn occurred only in one or two groups. You can then re-evaluate the marketing resources and retargeting efforts to those specified groups, adjusting strategy accordingly.

Different problems will predictably require different forms of analysis. While an issue like customer churn might lend itself to sentiment analysis, a problem like evaluating drug efficacy based on clinical trials may lend itself more to Bayesian inference. It’s important to understand why a particular statistical model might be more relevant than another before moving ahead with analysis.

Producing Insights Rapidly

Decision-making requires the delivery of insights in a timely manner. With analysis in hand, you need to quickly produce deliverables that will be presented to decision-makers. This means thinking about things like:

  • What sorts of reports to write
  • How charts and graphs may be integrated
  • What formats, such as dashboards, PowerPoint presentations, and white papers, should be used to provide insights
  • Who should receive the insights

It’s also important that the delivery of insights becomes a continual and constant process. Teams should be routinely working on projects, and there should be a strong emphasis on producing deliverables.

Driving Actions

Insights needed to be delivered to the right people. There’s no need, for example, to deliver actionable information that a purchasing agent needs to the company’s CEO. You want the fewest steps between insights and frontline decision-makers as possible. 

A data-driven company will make sure that purchasing agents have access to things such as real-time dashboards that show exactly what is trending, how inventory is holding up, and what items have the best margins. With the right processes in place, frontline decision-makers can log in to the system and see fresh insights daily.

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Big Data Business Intelligence Data Analytics

Why Companies Fail to Become Data-Driven

Investing in becoming a data-driven company is a common thread in discussions about the future of business. Unfortunately, a 2018 survey of major corporate executives found that the adoption of a data-driven culture is still lagging at many organizations.

Why are companies failing on data strategy? Let’s look at four reasons why and how they might apply to your situation.

Not Knowing How to Attack Multiple Opportunities

The lack of a solid data-driven culture is one of the biggest problems. This cuts to the core concept of setting the agenda. While a majority of the businesses from the study understood the importance of using analytics to make better decisions, a decided minority were taking advantage of secondary opportunities. For example, barely more than a quarter of the businesses had bothered to test the waters on monetizing their data.

Many companies don’t see data as a direct driver of profits. To this end, companies should consider things like:

  • Creating data-driven products, such as white papers and industry reports
  • Using data to drive interest in social media
  • Selling data directly to third parties

Focusing on Buzzwords Rather Than Action

Even major enterprises with respectable reputations have managed to fumble their “digital transformations.” While announcing a digital transformation is a good way to create a tech-savvy image and boost the share price, it’s not remotely the same thing as formulating a data strategy. 

To get the job done, you need to look at the following issues:

  • Setting internal standards for the acquisition, use, storage, and sharing of data
  • Installing C-level data officers and giving them the power to implement changes
  • Bringing all members of your company on board with the idea of transforming digital operations
  • Hiring professionals with backgrounds in programming, database management, AI, machine learning, and other technologies
  • Providing severance packages for employees and corporate officers who can’t get on board.

It’s important to take action rather than be the company that purely talks about data. A data-centric enterprise has an opportunity to improve processes, employee and customer relations, products, and services. Commit upfront to the process, and you’ll be amazed by the results.

Not Following on Successes with More Efforts

There’s a major risk that any digital transformation effort that fosters a data-centric culture will stall out due to its own success. Be wary of calling any effort quits without laying the groundwork for further successes. The job gets achieved, and possibly quite well, but then data becomes a complete project that fades into the past. 

A company can quickly become the proverbial hare that gets overtaken by the tortoise. While your operation might have done amazing work sprinting out to lead amongst competitors, slow and steady businesses that keep coming with new efforts will always win the race.

Struggling to Establish a Two-Way Street

People at all levels of the organization have to be able to communicate with each other. It’s also a smart idea to ensure that different departments are cooperating in ways that:

  • Prevent duplicating efforts
  • Offer options in an easy-to-understand way
  • Make the most of visualizations
  • Help parties understand what the data is saying.

Not Following the Data to Logical Conclusions

You can read about, listen to, and even develop some of the most valuable data in the world. But it’s important to try to see what the data pool is pointing to. This requires:

  • Experience working with math, data, stats, and probabilities
  • The ability to rapidly read small contexts while thinking macro 
  • Disclosure of all possible biases

Getting to the data-powered future is going to have more than its fair share of bumps. The important thing is to put data-driven shifts at the top of your To-Do list.

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Big Data Data Analytics

Are You Missing Data Hidden in Plain Sight?

Finding relevant data and effectively putting it to work within your company can feel like a massive challenge. Data sources, however, are abundant in many organizations. It’s important to get creative, however, in thinking about potential sources. Let’s look at where you can find valuable business data hiding in plain sight.

Looking at What Customers Have Said

Customers are a nearly endless source of useful data. You can use the people who buy your goods and services to source key data from:

  • Customer service inquiries
  • Sales data
  • Social media feeds
  • Website traffic logs

What’s great about customer-centric data is that it often can be used to draw a line from one bit of information to another. For example, using tracking cookies on your website can enable you to follow the progress a visitor makes through the sales funnel. You can watch how someone engages with a share on your Twitter feed to visit the website. That visit can then point to when they sign up for a newsletter, make a purchase, or download educational resources.

Publicly Available Data

Your tax dollars, in particular, pay to produce an ample amount of data. It’s available from sources such as the World Health Organization, Bureau of Economic Analysis, and European Space Agency. Yes, you’ll need to do some picking through the pile to find data sets that match your needs, but you also won’t be stuck dealing with licensing and usage restrictions.

There are also plenty of interesting publicly available data sources from private entities. Anyone who doesn’t have a Kaggle account, for example, needs to stop reading now and sign up. You’ll also find data in some odd places, such as the code-centric repository GitHub. Don’t be afraid to Google a topic as well.

Internal Data

Just about everything a company does generates bits of data. Suppose, for example, you want to figure out why personnel retention is problematic at your business. You should already have a database that includes data from the hiring and exit processes. There might be a trend developing in the forms filled out during exit interviews that shows employees leave because they lack opportunities for advancement. You can then formulate strategies for mitigating these concerns.

Notably, it’s important to start converting this sort of information into data. Don’t just let it languish on shelves at the off chance you’ll need it to defend against a lawsuit someday. Develop a process for converting entrance and exit information immediately into data upon receipt.

Required Reports

Reporting requirements, especially for publicly traded securities, have caused the generation of a massive amount of data. You can find a lot of numerical data at sites like Financial Modeling Prep and Quandl. It’s also possible to start looking at linguistic patterns in the written reports that are available from sources like the SEC.

Metadata

The data about your data is a source in its own right. Don’t be afraid to look past the simple numbers in the columns and dig into the descriptive elements of the data. If you’re going through your firm’s customer service data, you can look at relationships that arise from things like:

  • Time sequences
  • Person-to-person networks
  • Geography
  • Demographics

Metrics Produced from Your Data

Another approach is to use data to build new data. Suppose you have customer survey data available for each salesperson within your company. It ought to be possible to weight the different factors that drive success and profit. This information can then be used to create an index. Once each salesperson’s collection of data is compiled, you can then quantify their performance through a simple index that rates their work. Rather than sorting through 20 different data points, you can go straight to the index to start the assessment process.

Conclusion

Plenty of data is hiding in plain sight. It’s important to think about the different ways that data can be acquired and produced using these less obvious methods. With a commitment to finding data sources, you’ll be able to develop insights by working with information you might have previously overlooked.

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Big Data Business Intelligence Data Analytics

Continuous Intelligence: Continuous Pros or Cons?

Wooden human figure balancing wooden cubes with text PRICE and VALUE. Price and value balance concept.

With available data in the modern world emerging on a second-by-second basis, one of the most prominent areas of interest for organizations is the creation of continuous intelligence. A report from Gartner indicates that by 2022 more than half of all major business systems will employ continuous intelligence in their operations.

While this sounds like an exciting opportunity for any data-centric enterprise, you might wonder, though, what the pros and cons of utilizing continuous intelligence may be. Let’s break our analysis down along those lines to examine how a business might employ this emerging technology.

What Are the Pros of Using Continuous Intelligence?

By far the biggest pro of collecting and developing business intelligence continuously is greater opportunity. A company will have the opportunity to:

  • Constantly monitor developments
  • Stay ahead of less agile competitors
  • Look at issues with the most contemporaneous data possible
  • Produce analytics and work products on an on-demand basis

Consider how a bank might approach the use of continuous intelligence to handle fraud detection. As information about transactions flows in, the bank’s analysis operations would continuously stream data to machine learning algorithms. More importantly, as bad actors try to sneak around existing filters by adapting their tactics, the fraud detection system will note the tactical changes and adjust accordingly. Rather than waiting for customers to complain or relying on filters that can only be adapted sporadically, the bank will have a constantly evolving response to constantly evolving threats.

Enhanced predictability is also a major pro for continuous intelligence. A seaborne oil transportation business, for example, might use continuous monitoring of local news and social media feeds to detect upticks in pirate activity before tankers move into more dangerous regions. Decision-makers could be delivered a list of options that might include things like routing changes in response to threats, flying in additional security personnel, or notifying police and military forces in the area.

Optimization is a big opportunity for continuous intelligence. Global supply chains have come under increasing strains in recent years. Continuous intelligence systems can scan data regarding demand, supply, geopolitical activity, macroeconomic trends, and news reports to distill what the state of the market is. Rather than waiting for shelves to be empty or warehouses to be overflowing, the company could adapt in real-time to ensure goods are sufficiently meeting needs.

What Are the Cons of Using Continuous Intelligence?

The volume of data to be analyzed poses several potential hazards to organizations that depend on continuous intelligence. In particular, there’s a danger that voluminous data will create conditions where noise starts to look like a pattern. Quality control is a significant job when using continuous intelligence as a decision-making tool, and it’s critical to employ data scientists who understand the importance of always questioning the inputs and outputs.

Common human errors in handling analytics can creep in as well. With a continuous intelligence system constantly supplying insights, it’s easy to fall into the trap of treating the machine like it’s an oracle.

Additionally, dirty data is also a huge problem. If a system is feasting on continuous inputs, it can be hard to perform the necessary amount of quality control. Consider what happens if a data vendor changes the formatting of their XML documents. If the changes are implemented without notice, there’s a real risk that the continuous intelligence system will keep humming along while generating ill-informed insights.

How to Put Continuous Intelligence to Work

The adoption of continuous intelligence tools is a fact of the business world. If you want to dive into using it, it’s worth noting that you should already have a mature data operation in place. A firm that isn’t already generating numerous business intelligence insights should ease into continuous models due to the many real-time complexities involved.

Big Data capacity has to be built out in a way that exceeds what many operations currently use to produce BI. A company has to create an operating environment where bandwidth, connection speeds, and hardware are sufficient to sustain continuous data collection. For an enterprise that already has a robust Big Data department, though, continuous intelligence represents a chance to take their game to the next level.

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