Tuesday, February 3, 2015

Can your Analytics Initiatives Reach the Next Level of Maturity

It is the age of Big Data, data can be refined as information and knowledge, and  it's power for better decision making both strategically and tactically.

Big Data is a big phenomenon. Some call contempary time the data "Field of Dreams," build it and the users will come. However, most of the organizations didn’t achieve the expected result from their data deployment effort. To get people use the data and change their processes to align to what the data is telling them is an emotional problem. What is the perspective - will these cognition gaps start disappearing, organizations working through to the final hurdle, and finding the ways to spark the imagination of the average user to get them on board; can you find the right catalyst to get the widespread adoption that is needed? Or to put simply, can your analytics initiatives reach the next level of maturity?

One aspect that is often forgotten is the fourth V of the Big Data: Value. Data is there, growing daily at alarming rates, the point is how organizations are taking advantage of it effectively. Lots of organizations have embarked on big data strategies pushed by hype, the perceived low cost of storage / processing technology and unrealistic expectations that creating supply will automatically generate demand. The significant failure is that many ask for lots of data, in the hope that something will yield insight. But in the end, since they didn't know what they were looking for they never found anything, and the volume of data probably only made it harder. So little gain, for a great cost. There is a need for better user adoption of tools available to help manage the volumes of data being collected and stored. More user-friendly applications can allow most business users insights into their own data on the fly, without reliance on IT or third parties etc.
It needs to "be going back to basics." Analytics/BI initiatives fall by the wayside because they are not perceived to have an impact on the business as a whole. Does It need to be going back to basics by self-checking: What type of industry are you in? How do you measure the relevant KPIs of your industry? Of your company? How are your peers/competitors performing? What is your strategy for X period? How do these KPIs reflect your progress/ achievement? Are those KPIs defined by functional areas and will these be relevant within the overall planned/expected objectives. Then, applying standard BI/Analytics or sophisticated predictive modeling, or using Big Data or conventional BI practices/methodologies, at the end the important point is that whatever is produced/delivered, by whichever mean of visualization, it contributes with Value to the decision makers. The technologies and methodologies are multiple and they all seem to work, but it's more of an understanding of your business, your industry, your objectives, your strategic plan, and then using the technology to get the results (KPIs) to make better, more accurate and timely decisions.

If the catalyst event is a business imperative it will have broad organizational support: Teams will be compelled to get onboard and support the change. If the reports / models you have been using are all going to break, you are more willing to think and act differently. You also need to educate, guide, convince and get buy-in from executives and users to make these projects a reality; and data-based decisions are not mere discrete moments within some form of static personal domain, but rather a flow of patterns, think floating to spur the fountain of creativity, where you are moving to the flow of life around you, but understanding the nature of your world, decisions within that systems understanding, help guide you towards your destination a little easier. The phase "data over-consumption" is the key phrase. If an organization becomes data-driven but simultaneously simplified their business processes and infrastructure, there would be no "hangover". The former implies that the business demands the data and its insight and the latter ensures that that increased insight did not come at an increased cost.

It is perhaps the time for reassessing the way data analytics works to respect and accommodate the data-driven approach. First of all, do the extra data elements that can be stored have any actual value in and of itself? Second, and this has been true of data management forever, data that is not correlated, integrated and understood may as well be useless, both in the eyes of the consumer and intrinsically. Thirdly, very often there is a lack of understanding of hidden costs: allowing application owners to dump data into a big data storage, without adding context and semantics to the data, often simply transfer data exploitation costs to the consumers. Fourth, and it is true as of any other engineering discipline, retrofitting major capabilities (quality, ease of access, self-documentation, etc.) unto an existing data management that hasn’t contemplated them from the start is often much more expensive, if at all possible, than doing it right first time. So, in short, any data management (big or otherwise) program of work should be very much, as always, focused on business drivers, ROI based on solid on cost / benefits analysis. Whoever is doing the review of the analysis should be very alert to vague promises of build and they will come.

It is the age of Big Data, data can be refined as information and knowledge, and  it's power for better decision making both strategically and tactically; it is the time that analytics should flip to the new page of reality and reach the next level of maturity.


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