Is your analytics strategy in place?
Data analytics strategy
Predictive and big data analytics are the buzzwords within the IT sector at the moment. Data can tell all kinds of unimaginable stories, but only when analysed well, and analysed fast. Hidden within analytics are customer and business insights that can help companies stay ahead of the competitive curve. Combining big data with breakthrough technologies such as high-performance, in-memory, predictive analytics tools and cloud technologies can pave the way for business transformations and new revenue opportunities. It is obvious then that in their own best business interests, enterprises need to invest in an all-encompassing analytics strategy and subsequent initiatives to put the strategy into practice. A successful analytics strategy is informed by three key considerations:
Getting the right data
An enterprise can have the most sophisticated analytical tools but to no avail, in the absence of reliable, updated data from credible and relevant sources. The source data from which everyone is running different analytics needs to be unified and consistent, so that the starting point, or the premise from which everyone is deriving their interpretations, is the same for all teams involved in the problem-solving and decision-making process. Teams can work collaboratively only when they have the right, and the same data. What constitutes “right” data is also dependent on what the business requirements and the expectation of output is, from the users.
One of the biggest data-related challenges that companies tend to face is the existence of many disjointed data sets, maintained by different business functions, at various points in the data gathering journey. Many times, inadvertently, important business decisions are based on outdated and inconsistent data.
Picking the right analytical tools
Let’s say this upfront — a single BI or analytical tool can rarely meet all of the needs of an enterprise. In addition to that, most teams and individuals will gravitate towards tools that they are already well-versed with, even if they might not be the best options available for a particular kind of analytics. The result of using multiple tools to perform the same analytical task is that the results cannot be tested and reproduced. Using multiple tools also drives up the cost of licensing, because bulk discounts cannot be availed of, while at the same time IT costs for maintenance and operations. Using one primary BI tool might be problematic as well because a single tool might not offer predictive analytical modelling, forecasting, or might not have in-built capabilities to automate processes. A single-user tool might limit the possibility of errors, but it also slows down the process because people can’t collaborate in real time. For all these reasons, enterprises need to figure out a stack that consists of a mix of analytic tools that allow for scalability, collaboration and cost-effectiveness while serving its primary business function.
Making data accessible to all
Restricting access to data pigeonholes employees into using it in an unimaginative, prescribed way. The beauty of data paired with sharp analytics tools is that the same set of information can lead to a plethora of insights, observations, and astute observations about future trends that can inform business decisions to give companies a great competitive advantage. Making data accessible to every stakeholder empowers employees to experiment with it in creative ways. However, the benefits of ease of access to data is directly proportional to how quickly it can be accessed. Here, what needs to be considered is what the business requirement is, and how much accuracy that requirement merits. Losing weeks in establishing the accuracy of a data set, when the business requirement needs only directional data to make a sound decision is counter-productive. Which is not to say that a controlled environment must always be sacrificed in the interest of speed. With the right kind of tools, it is entirely possible to withhold sensitive data, while making data available to large groups of people.