Sunday, November 1, 2015

DIY Analytics – Is it right for you?

As I consider the all-too-long list of things that need fixing in my house and watch yet another spot from the big-box home improvement retailers, I realize why the Do It Yourself (DIY) trend has legs. You know your own house and DIY gives you control over the schedule, quality, cost, and outcome of the work. Of course you had better have the necessary expertise and can acquire the right materials and tools. If you don’t have that level of confidence, you outsource the work and hope you can keep any eye on things and get your money’s worth.

The large scale home improvement retailers succeed by making the materials, tools and expertise available and supplying that confidence. Many times, you start with something easy, it works out and you feel empowered. Other times, you take on a little too much, your work does not hold up and you end up calling in the pros and putting your tools away.

We often see the same story in business analytics. Finance, marketing, HR, and supply chain specialists are lured by tool vendors into thinking they are better off with DIY analytics and freeing themselves of the IT pros with their long schedules, hefty price tags and results that can be, shall we say, less than satisfying.

In this case, the folks that make money are the BI and Analytics (BIA) technology vendors who convince business organizations that DIY Analytics is the way to go and happily sell tools and some training.  Then they move on to the next sales cycle. Sometimes it works out, but often these applications fall apart over time for lack of reliable raw materials (data) or solutions that are not built to last and cannot be adequately supported by those who developed them. At this point, the IT pros are brought back in to fix things and the tools end up on the shelf.

There is a better way. IT organizations have found success by taking a lesson from the home improvement retailers and supporting DIY analytics successfully. They do not insist on building all the reports, dashboards, and analytic applications themselves. Instead, for those customers that prefer the DIY model, they provide a set of shared tools and trusted data. Their customers then build and enhance the application to their own preferences and on their own schedules; while controlling costs by paying for much of their own labor.

There is a critical ingredient that is necessary to make this model work though. The home improvement retailers enable their customers with expert advice, training videos and communities of other DIYers sharing knowledge. IT shops can provide that same kind of support structure. It often comes in the form of a formal dedicated organization within IT. I have seen it called a BI Community of Practice, a Competency Center, or a Center of Excellence. Whatever the name, the mission is the same: Crate a resource that gives its customers the necessary technology resources to succeed, and the confidence to do it themselves with a flexible set of support models and services best left to the pros. These include everything from security, backups, capacity planning, performance tuning, professional training, documentation, and proactive knowledge sharing that helps the entire community use their resources efficiently and effectively. This creates an environment where the entire business wins with better service, better decisions, and better performance. Oh, and when the pipes leak, just call a plumber.

Monday, July 6, 2015

Cyber Security and Business Analytics: Imperfect Together

This week, I was reading an excellent piece here about the cyclical nature if the Business Intelligence/Analytics industry (BI). The assertion here is that priority tends to swing between periods of high business-driven enablement and IT-driven governance.  The former tends to be brought on by advances in technology, and the latter by external events, regulation, and necessity.  We are currently at the apex of an enablement cycle at the expense of governance. One casualty of lax governance is often cyber security.

Recently, we have seen a rash of high-profile data breaches. One of these was the large scale theft of data from the health insurer Anthem. This one was notable as it was the result of a vulnerable data warehouse where sensitive data was left unencrypted.

Those of us who practice BI and Data Warehousing professionally have a paradox to deal with. We have always been evaluated on our ability to make more data available to more users on more devices with the least effort to support business decisions. In the process, we tend to create ‘one-stop shopping’ and slew of potential vulnerabilities to those who would access proprietary data with criminal intent.

The software vendors in our space have been all too complicit in this. After all, what sounds better to the business decision-makers they market to: “multi-factor authentication” or “dashboards across all your mobile devices”? “advanced animated visualizations” or “intrusion detection”? “data blending” or “end-end data encryption”?

How about “self-service business analytics” or “help yourself to our data”? Consider how easy we make it for the users in an enterprise to export just the useful parts of a customer database, along with summaries of transaction history to a USB stick and walk out the door with it?

This idea that BI and data warehousing requires more attention to security is starting to gain traction, however. A quick web search reveals that the academics are starting to study it and the leading established vendors in the space are starting to feature it in their marketing in ways I have not seen before. See the current headline on the MicroStrategy website for one example.

The main takeaway here is that BI and data warehousing practitioners need to consider cyber security in architectures and applications the same way it is done in transaction processing:
  •          Get a complete BI vulnerability assessment from a cyber-security professional
  •          Calculate the expected value of an incident (probability of an event times the cost to recover) and allocate budgets accordingly
  •          Demand proven security technology from your vendors and integrators around features such as authentication, end-end encryption, and selective access controls by organizational role
  •          Don’t be afraid of the cloud. The leading vendors of cloud services employ state of the art security technology out of market necessity and are often the most cost effective solution available.

What’s old is new again - BI edition

Those of us with a long history as business intelligence (BI) practitioners have pretty clear memories of all the days when we saw an overhyped technology promise to change the game by freeing business organizations of IT tyranny with a new class of products that made self-service reporting and analytics better, faster, and cheaper. We saw this with the arrival relational databases. Believe it or not, they were originally all about data access not transaction processing. We saw it again when Online Analytical Processing (OLAP) was available on top of Online Transaction Processing (OLTP).  OLAP brought data access directly to our spreadsheets and PowerPoints where we really wanted it. In both cases, business organizations bought this technology and built organizations to use it thinking they could declare their independence from IT. It worked splendidly for a while. IT created extract files from their applications and celebrated getting out from under a backlog of reporting requests. Businesses felt empowered and responsive as they created reports, dashboards, and even derivative databases integrating internal and external data within their siloed subject areas.

Then reality set in.

All these new products required maintenance, documentation, training, version control, and general governance. “Shadow IT” organizations sprung up. They often became, in aggregate, far more expensive and just as cumbersome as what they replaced. Worse, the software vendors happily exploited this balkanization of larger organizations by selling redundant technology that had to be rationalized over time causing licenses to become unused and not transferable. Wouldn’t it be nice to buy a slightly used BI software license at a deep discount?

The fatal flaw in this arrangement is the proliferation of overlapping and inconsistent data presentations that we call multiple versions of the truth. These create mistrust and cause executives to go with their guts in lieu of their data.

Each of these technology advances, along with even faster hardware evolution, did have the impact of making decision support and analytics far more powerful even as the open source movement made it more accessible. This, in turn, created competitive advantage for those who learned to exploit it and made a strong analytics capability mandatory in today’s commercial climate.

One problem still remains. As we like to say, you can buy technology, but you can’t buy your data. Today’s analytics require integrated and governed data across finance, operations and marketing, online and offline, internal and external.

That brings us to the current generation of revolutionary BI tools like the latest data visualization technology that is all the rage right now. (I won’t name names, but think “T” and “Q”.) Just like the previous BI waves, they exploit technology advances very effectively with features like in-memory architectures, wonderful animated graphics for storytelling and dashboards, and even data integration through “blending” and Hadoop access. These products have been hugely successful in the marketplace and are forcing the bigger established players to emulate and/or acquire them. The buyers and advocates are usually not IT organizations, but business units who want to be empowered now.

What does this mean for business decision makers? Just like the technology waves that preceded them, these new visualization tools do not address the organizational and process requirements of a highly functional and sustainable BI capability. Data and tools must be governed and architected together to create effective decision support.  Otherwise, you end up with unsupported applications producing powerful independent presentations of untrustworthy data.

We have seen this movie before and we know how it ends.

Mr. Robinson is currently a Business Intelligence and Analytics consultant with Booz Allen Hamilton. He has previously held practice and consulting leadership positions with Ernst & Young, Oracle, Cox Automotive ( and Home