7 Tips for Transitioning Data Insights into Business as Usual

7 Tips for Transitioning Data Insights into Business as Usual

7 Tips for Transitioning Data Insights into Business as Usual
As a Data Analytics Consultant with experience in multiple industries including Telecommunications, Health Care and Retail, I have come to learn that effective …
“Progress is impossible without change, and those who cannot change their minds cannot change anything”
– George Bernard Shaw
The Irish playwright was on the money – no matter what way you look at it, attacking an old problem with a new solution is going to require change. In particular, attacking an old problem with advanced predictive analytics is going to spur an array of changes to business processes and with this, a necessary change of perspective. Staff will now be tasked with using the results of models to prioritise who they target with marketing materials, who they call and which products they promote to them. In times of transition, staff can feel overwhelmed, confused and frustrated if communication is poor. In fact lack of staff buy-in is one of the top reasons for the failure of newly adopted predictive analytics solutions. Consider the following scenario (it might be all too familiar) –
Charlie, the Head of Innovation, had been tasked with finding a new way to boost sales and drive efficiency in the marketing department. With the pressure of the management board to use the ever dwindling budget to get major return on investment, he hired Angela to head up the new Data Insights division.
Angela, an expert in handling big data, has built an impressive customer churn model to get the ball rolling. She fired a quick email over to Dave in Account Management to get him up to speed with the new scored lists of customer to target.
Dave opened the attached document from Angela and his heart sank, “decision trees… boosting… contingency tables…” – none of these words meant anything to him. It was only last Tuesday he found out that the department would be required to use a new system to determine which customers were likely to leave the company. Dave didn’t know how he was going to fit this in around his already incredibly long call list and quarterly reports.
It’s not a pretty picture – anxiety, uncertainty, limited communication.

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Each party must be informed, bought in and committed to the new system for it to be fully effective. But how can this be achieved? How can the sting be taken out of change management? The following are seven tips to enable proactive engagement with change and to bypass stress at the outset of a predictive analytics project.
Start small, talk business
Has the company used predictive technologies before? What is their appetite for innovation? If the answers are no and small, consider the speed with which you plan to make operational changes, the scale you adopt for the first model roll-out and even the impact of buzzwords or jargon during discussion. When tailored to the audience, a clear presentation of the new approach and its benefits will go a long way to mitigate most fears and concerns.

Include the business from the beginning
The buy in from department heads and those who will be implementing the results is absolutely key for success.

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