Rescuing Businesses from the DIY Big Data Vendor Trap

It is well known that an explosive combination of Big Data growth, digital storage capabilities, and technological advances has forever altered the modern business analytics landscape. At the same time, current economic demands require ever increasing efficiencies in time, labor, accuracy, and expense. These efficiencies are not unique to any single industry but are desired—often mandated—across all market segments. Many companies have heard enough to know they should consider investing in data analytics as a means to achieve these greater efficiencies; however, many data analytics software tools used in such pursuits are not sufficient to achieve maximum optimization.

Too many potential customers?

Recently, I received a product positioning template to be completed for our advanced analytics platform, STATISTICA. It's a fairly standard guide: define the market personas, the industry problem, the available solutions, etc. Such a template helps us organize our information so that potential customers can assess product relevance.

So, when I came to the section where I was to describe the "industry problem," I was stuck, because every industry has its own kinds of problems that can readily be solved with analytics. And there are so many industries that can benefit from so many solutions—I could not pick just one! I almost started to write a boilerplate description of a landscape of generic businesses seeking to increase productivity, control risk, reduce waste, or streamline operations. Of course, these are businesses who need advanced analytics, right? And then it finally hit me: there is at least one common problem that every business must face in every industry: a marketplace full of software solutions that all promise to reap the benefits of [insert latest analytics buzz phrase here].

From a provider’s perspective, the problem is not that organizations need comprehensive solutions to optimize efficiencies in a tight economy. Such an environment is actually ripe with potential for enterprise-level providers like us. We would see this as a boon, not a problem. Rather, the real challenge is convincing them of the depths of their own need.

What’s your problem?

There are so many organizations that have already invested time and resources into solutions that are not really designed to maximize end-to-end business potential. And the steps necessary to bring them up to speed are hampered by a lack of education and lack of infrastructure that produce or worsen inefficiencies.

For instance, decision-makers may have insufficient understanding of what data analytics is and what it can do, and how it relates to business goals. Accordingly, they are susceptible to misinformation and marketing hype, and they may not fully appreciate the breadth of business goals that they can or should achieve. And those in the midmarkets may also believe that data analytics is something that can benefit only organizations larger than their own.

Decision-makers may also tend to consider analytics with a DIY mindset, believing that such an approach will prove cost-effective. Accordingly, they will see little value in hiring consultative experts to develop practical strategies and applications, and they will struggle with self-guided investigation and implementation of possibly ill-fitting or incomplete software solutions.

Furthermore, organizations may have an inadequate infrastructure to collect, manage, and/or process data that is relevant to their business goals. Accordingly, company leaders may be unable to execute upon the goals they have devised, due to data warehouse incompatibilities, processor limitations, personnel expertise gaps, etc.

Cobblestones are bumpy and obsolete

The majority of analytics tools on the market are specialized rather than comprehensive. They are not all real-time, and they are not all user-friendly, and they are not all compatible with other software data systems. Most are not predictive, or they are not rules-based, or they are not easily adaptable as business circumstances render data models irrelevant over time. Some tools suffer data volume limitations, or they are not supported by a dependable R&D authority, or they do not produce results that can be validated (e.g., pharma).

In this highly segmented software environment, organizations often cobble together a mix of tools for their users with disparate levels of analytics savvy in order to accomplish business goals, and all the while they may not even be aware of what analytics opportunities they could be missing.

While these various, specialized analytics tools may produce very useful niche solutions, efficiencies are optimized only when these solutions can be applied across an enterprise within a single, robust platform that takes advantage of leading-edge technologies to meet all unique business requirements.

But will any of this help me finish my template?

Ruminating on this situation will probably not help me finish my positioning template. However, I do know some organizations will proceed down the DIY path. Others with greater investigative resources or with tighter competitive margins may conclude early on that such a path will not lead smoothly to the level of efficiencies they seek. In either situation, the use of a single big data platform that ties into existing data repositories and communicates readily with existing software infrastructure—a platform such as STATISTICA Enterprise—could provide a real boon for them. Maybe they just don’t know it yet. And it’s our job to let them know.