Changing Environment is Changing Analytics

Looking back across the last ten years working with analysts in a variety of industries, I realize how much has changed in the environment. As with any evolutionary process, this change in the environment means analysts have had to adapt accordingly. Three changes in my mind have had the biggest impact.

Looking back across the last ten years working with analysts in a variety of industries, I realize how much has changed in the environment. As with any evolutionary process, this change in the environment means analysts have had to adapt accordingly. Three changes in my mind have had the biggest impact.

More data! Despite the increased strategic importance of analysts, there is a lot more data to contend with. Like doctors who loathe the internet due to the increased time they must spend un-diagnosing for their patients what a clever online algorithm proclaimed, analysts must fend off misinterpretations of myriad data points brought to them by those perhaps unaware of the pitfalls of a lack of research rigor. Data is now everywhere, in various formats, of various types. This provides analysts with much more data with which to create their stories, and it doesn’t always line up. This can be paralyzing for the perfectionist trying to back up every implications or forces analysts to put some value on their inclination which is not something condoned by the industry accepted analyst personae. This glut of data also has a lot of analysts tied up in complex integration projects that continuously chase a moving target and don’t represent what analysts love to do, which is draw mighty implications that change the world (or at least the department). However, analysts complained for decades they wanted more data, so perhaps a lesson in being careful what you wish for.  Today’s analysts don’t want data, but more tools to consolidate and integrate to make interpretation simpler and faster, due to the next point.

Less time! Whereas it is debatable whether some data needs to be real-time, it is a cool marketing stamp that data suppliers love, so real-time data is growing in volume. Consequently, real-time implications are becoming expected. Systems to create real-time data are rampant, but systems to process and interpret them have not quite caught up. Not so long ago, analysts could spend months as part of the strategic planning cycle making sure their implications and recommendations were thought through, reviewed, stakeholder approved, and even polished for beautiful presentation by artistic graphic artists. Today, yesterday’s results require interpretation today so implications can be presented tomorrow and action plans can be executed by end of week. Statistical models are flourishing, algorithms are flooding, and sexy BI tools are growing in the market, but they are still trying to catch up with all the data.  And like all wonder drugs, they seem to come with that warning to check with your analyst first before believing all the automated conclusions.

More importance! You will hear everywhere that corporations are putting more of a focus on fact-based decision making. This nomenclature must be anathema to the ageing baby boomer, making it seem like decisions back in the day were based on gut and emotions. However, there is an increased expectation that decisions be defended with rigorous metrics and that all goals and objectives have clear measures attached to them that must be monitored often. This dramatically increases the power of the owner and manipulator of the data (the analyst), who is the expert at assembling the data into an implication that ideally drives action.

Overall the opportunities for analysts to influence important decisions are growing. And what every analyst wants is to influence decisions based on sound interpretation of solid data.  Analysts can now find themselves in the middle of the big show, often holding the balance of power by being the owner and interpreter of the facts.  But like dynamic orators skilled in the art of rhetoric back in the first senates, all analysts know that the selection and presentation of the data can easily lead to many different conclusions based on what is chosen to show or not show. With great power comes great responsibility. The opportunities are better than ever, but a need to stay objective, as unbiased as we can, and to consider all strong challenges is paramount.  We must be ready for the hard debates and not become that expert witness ready to defend any point for a fee.

This blog was produced as part of the very first Analytics Blogarama – a one day event where bloggers share their individual views on a common theme.  

Today’s theme is The Emerging Role of the Analyst. To read other viewpoints, please visit the blogarama navigation page.