It seems like it’s all about data analytics these days.

You can’t go to a conference or look at an article without hearing or reading something about data analytics. As an industry, we’ve gone from the recognition that we have all of this data (i.e. Big Data) to focusing on how to derive insight and value from that data. It’s been part of our world for so long that the term “Big Data” isn’t used in the same way it was before —we just talk about data. The scope and complexity of it is understood.

Large, global enterprises are already doing great things with data analytics. They are pushing the envelope, forging ahead, and some are even infusing artificial intelligence and machine learning into their business operations.

In fact, Gartner predicts that 90% of large organizations worldwide will have a Chief Data Officer (CDO)—a person responsible for managing corporate data as an asset and using it to create business value—by 2019.

But what if you’re not a large, global enterprise? Does that mean data analytics is beyond your reach? Must you hire data scientists in order to leverage analytics?

I would say no.

Start with the Basics

If your business isn’t a large enterprise, it’s easy to feel overwhelmed when you think about data analytics. There is a notion out there that you need a big data platform or big data strategy before you can leverage analytics. But it’s not correct.

Data analytics, in layman’s terms, is the process of gaining insights from data. And you’re probably doing some form of it today. If you’re combining data from different sources into a spreadsheet and using pivot tables, charts, and graphs, you’re doing simple data analytics.

If you’re just getting started with data analytics—or restarting new projects in that area—it’s best to focus on a specific initiative, or use case, first. There are four primary functional areas of data analytics: data warehousing, visualization, business intelligence, and cognitive computing.

Data Warehousing

A data warehouse is what it sounds like—it’s a place to house data. Enterprise Resource Planning (ERP) systems, sales and marketing automation tools, finance applications, supply chain systems and many other applications create data.

A data warehouse is a repository where you can store data from these different sources for the purpose of performing analysis. Data that resides in proprietary databases or spreadsheets or flat files or some other source can be imported into a data warehouse and used to gain insight into your business by creating the “single source of truth” that is critical to effective and accurate analytics.

Data warehousing has been around for years. But if don’t have one in place, it’s where you may want to begin. Once you have your data in a data warehouse, you can then start down the path of more sophisticated data analytics.


The value of data is diminished until it’s viewed in relationship with other data and put into context. Visualization is the process of taking data and presenting it in human readable format that adds context and relationship.

A good example is a spreadsheet. By itself, a spreadsheet is nothing more than rows and columns of data. But if you take that data and create a pie chart or a graph, the data means something. It now has context, and you can use it to make decisions.

The more data you have, the more sophisticated analysis you can perform. You can use visualization tools to correlate many combinations of raw data and derive meaning from it. And that enables you to make decisions faster and take more informed action.

Business Intelligence

Business Intelligence (BI) has also been around for years. Volumes of books have been written about it. And it’s not a simple subject. But in the most basic sense, BI tools have one purpose: To help you answer the question, “How is our business doing?”

You start with defining key performance indicators (KPIs) that are meaningful for your business. Then using BI tools, you can map real-time and historical data to those KPIs. In doing so, you get an assessment of how your business is performing—at a specific moment in time, or based on trends, leveraging the most accurate data points. That reveals insight into your business you otherwise may not have had and helps you make decisions on what to do next.

Cognitive Computing

Cognitive computing is the ability for computers to mimic human thought and reasoning and make human-like decisions through the use of data mining, artificial intelligence (AI), machine learning (ML), and other methods. Great strides have been made—think self-driving cars, autonomous taxis, and predictive maintenance—but we are just beginning to scratch the surface of what is possible.

When you think about it, there is almost infinite potential. And businesses and software vendors are expected to invest heavily. By 2020, according to Gartner, artificial intelligence will be one of the top five investment areas for 30% of CIOs and nearly all new software products released by vendors will use AI technologies in some way.

There is still a lot of work to be done. But what is possible today, and what the future holds, for artificial intelligence and machine learning in business and IT are exciting.

Keep it Simple

Data analytics can be confusing and somewhat intimidating. There are so many opinions and promises out there that it can be hard to choose a path forward. It’s important to take an honest look at where you are and make a decision that is right for you and your business.

Keep it simple at first – perhaps select a single use case that is shorter in scope yet impactful to your organization. Then progress into more complex areas when it makes sense to do it, and your organization is ready for it.

For help developing a plan to leverage data analytics in your organization, click here to get in touch with us or contact your ePlus Account Executive.


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