3 Reasons Faster Achieves More in Analytics

Interested in a counter-intuitive statistic? In one survey successful business intelligence project leaders reported getting value from their implementations more than twice as fast as those that failed to meet their objectives.  A natural tension usually exists between speed and impact.  When one is prioritized in technology, too often the other suffers.  As indicated in the survey above, data analytics and businesses intelligence initiatives don’t behave that way.  In fact, speed and impact have been shown to correlate.   The causes and consequences of that statistic can be baffling, because it would almost lead one to believe that the bigger of a hurry you are in the more value you will get from your efforts, which is not the right conclusion to draw.   Below we have boiled down the 3 reasons why speed and benefit correlate so often in data analytics.

Competent Teams Execute Faster

Teams that claim they met their business objectives are also more likely to achieve solutions that involve advanced analytics like machine learning and average twice as many users in their systems as those that claim they did not meet objectives.
Want to move fast?  Strong expertise in delivering value is your best bet.  A classic and thorough MIT/Sloan study comparing performers to strugglers cited reduced time to value as one of the key management priorities for getting the most from analytics.  Technical expertise, while important is not the whole story.  A team that is responsible for a project’s impact must understand how to organize the heterogeneous skills required, how to break a project down into realistic stepping stones that reduce risk and how to measure progress to keep everything on track.   That means regularly measuring the results of the project against the predicted impact to the business and making adjustments where risk presents itself.
An interesting aside is that these are the same teams that don’t hurry when it comes to getting the fundamentals right.  Getting the business case in place, making sure the stakeholders are involved are important management activities.  They also don’t hurry when it comes to the information.   They know these projects only succeed when they produce high quality and well defined information.   They define clear and helpful practices at the tactical level that enable agility.   These are the teams that keep business rules organized, data dictionaries up to date and even involve the stakeholders in the process of data governance and accountability.   In software development, the concept of technical debt applies.  They don’t take short cuts in their projects they lead to numerous manual fixes later (essentially paying interest on their decisions for the long term).   They save time by executing on answering questions and getting across the line with a strong foundation of understanding.

Executive Involvement

Succeeding teams were 4x as likely to have senior leadership involved and 59% report executive involvement was a key success factor.  For teams that didn’t meet objectives, 73% claim executive involvement was a key challenge.
While this follows from above, it is still possible for a company to believe a data solution is important but fail to get the right CXO involved.   That spells doom.   This one area of data analytics implementation that has been fairly well studied and should be a natural intuition for most implementation teams.  However, the number of teams that still take on unified data strategies without proper executive sponsorship is baffling.   The symptoms are clear, you get frustrating project interruptions, solutions that start fermenting from individual stakeholders so they can manage their day-to-day information flow and often the team drowns in lengthy tool evaluations.   That last one is a surprising side effect, but usually comes from the team’s request for tooling budget being one of the primary sponsorship components it needs to answer to the management team on.  As a result way too much time is spent on the technical stack rather than the impact to the business.

Clearly Defined Business Cases Get the Right Investment

64% of teams that self-report their company doesn’t meet its objectives in analytics also reported leadership for the projects was below the C-level.
Before embarking on this effort consider the business case carefully.   Don’t think in terms of “I’m curious about” or “It would be nice to have …”.   Think in terms of “If the company could answer this question every week our operating margin would be 10% higher in 6 months”.  Increase cash flows, reduce expenses … whatever.  That kind of quantitative impact is intimidating, because it means holding yourself to objective results in an uncertain environment.  Don’t avoid that fear, use it.   Force yourself to do the homework and make the case that gets senior leadership to care deeply about this endeavor.  With that comes a willingness to invest in it and a vested interest in your project’s success.  You’ll treat the project as more important too, and have a clear marker of success to earn.
These three points are a great starting point to avoid being one of those teams who several months later can not determine why they keep getting stuck.  When that is exactly what 86% of teams report, the case for taking the time to get these right is evident.
By | 2018-04-04T11:40:51+00:00 April 4th, 2018|Data Practices|