“There exists a significant opportunity to create a symbiotic relationship between business and IT.”
One founding principle of data warehousing since its inception in the mid-1980s was a separation of operational (run the business) and informational (manage the business) activities. As I pointed out in 2009, this ancient postulate dated back to more than a decade earlier. It arose for two distinct reasons, one technical and the other business.
The technology issue was simply that there was no other way to get and use data for management reporting or analysis other than to extract it from the source systems and use it elsewhere. The business reason was that decision makers of the time valued data consistency across sources more than timeliness of delivery. In fact, many preferred the certainty of end-of-day figures to the “noise” of ongoing business. These reasons were largely superseded by the beginning of the millennium but, it being the nature of postulates, few people really noticed. Fewer still did anything about it.
In this manner, very different roles emerged in both business and IT for the operational and informational environments. Operational IT people focused on process; informational IT / business intelligence (BI) people majored on data. On the business side, operational people got things done immediately; decision makers thought about what happened or what to do about it at a more leisurely pace.
Of course, as business sped up through the 1990s and 2000s, operational data stores (ODS) and operational business intelligence systems accelerated the delivery of data to decision makers. However, in most cases, the operational/informational architectural divide was preserved and data was just passed more quickly or frequently across it. The IT and business roles were left unchanged. Arguably, it was this failure of organizational change that most impacted the uptake and success of operational business intelligence then. Now, as we begin widespread implementation of Internet of Things (IoT) projects, where data flows in real-time abundance and decision making must inevitably follow, tackling this organizational deficit becomes imperative. But how?
A range of changes are needed within existing organizations, spanning from top to bottom, and which I’ll discuss over this and succeeding posts. I start here with changing the culture of the organization and will discuss changing business roles next. Following that, I’ll look at IT roles and, particularly, those needed to bridge the current gulf between business and IT.
Business-intelligence system: a biz-tech culture
How often have you heard business people complain that IT just gets in the way of doing business? Conversely, how often do IT people accuse the business of changing their minds or simply not knowing what they want? This “business-IT gap” is widespread among all organizations—and spreading wider daily within them. Closing the gap requires behavior changes on both sides. But first, it needs a radical change in thinking. That’s why I started talking about the biz-tech ecosystem in my book, “Business unIntelligence”.
Technology—and information technology, especially—is at the heart of all business innovation and success today. This requires symbiosis between business and IT people: business people must understand information technology and IT people must know the business. This sentiment is not new, of course. However, directed action is necessary to make it a reality. Nowhere is this more possible and needed than in the current movement toward real-time analytics.
To manage their business, decision makers have traditionally used business transactions. These legally binding interactions occurring between organizations and/or people are now well understood and mostly well governed. Business people can use such data with some confidence in its consistency and cleanliness from data warehouses already provided by IT.
The data now available from social media and increasingly from the IoT is very different. It represents events and measures of everything that is happening in the real world. IT has still to fully come to grips with its characteristics: size, speed, reliability, ingestion, contextualization, meaning, and more. Data scientists are already playing with it. Business people are chafing at the bit to derive ongoing value from it, without seeing the dangers inherent in the above characteristics.
Business already understands that the greatest value of such data is while still fresh: (near) real-time analytics. However, only IT—and, in particular, business intelligence people—can create an environment in which this value can safely emerge. There exists a significant opportunity to create a symbiotic relationship between business and IT here. IT can help business understand what is really technically viable. Business can show IT how value can be gained from streaming data. Working together in a biz-tech ecosystem will reliably drive value and innovation from a business intelligence system built on real-time analytics.
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