Analytics: Do you have a strategy?

Big organisations want to capitalise on the data exploding all around us

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Big Data and Analytics are buzzwords and hot in the corporate world. Every enterprise worth its salt has embarked on a journey to capitalise on the digitisation and explosion of data around us. And, why not? The payoffs are alluring indeed. It’s known that the four Vs – Volume, Velocity, Variety and Veracity of data – combined with the declining cost of computing are driving this phenomenon.

While data is key, it is easy to lose sight of outcomes. In my previous blog, I talked about six ways to avoid the pitfalls of analytics becoming just another data set. But when embarking on the analytics journey, a well thought out analytics strategy is essential to truly capitalise on this growing trend.  Such a strategy would take an end-to-end view of what is sought to be achieved and mobilise the resources and corporate focus needed to get the desired outcomes. Here’s a five step plan to get the desired outcomes from your analytics strategy:

1. Begin by understanding the end Begin evolving your analytics strategy by understanding the value delivered at the end of a process. At a high level, this value could be revenue growth, improved profit margins, higher CSAT, improved cash flow, or even improved ability to meet regulatory norms. The process owner must clearly determine the outcome he/she hopes to achieve out of the process.

2. Don’t lose sight of your KPIs
Align the data to the KPIs which are set to be monitored or improved. This second step essentially means aligning the business intelligence (BI) strategy to the analytics strategy. The ‘Chief Data Officer’, which could be the CTO who usually owns the BI setup, should be responsible for getting all the required data together within the right time frame to support data-based decision making.

Most global enterprises suffer from a siloed approach to managing data flows. This makes analytics a subpar exercise, stifling the outcomes that can be achieved. Bringing all the data together is possibly the single biggest challenge which the BI team needs to address. In addition, most enterprises, at a given point cannot work on more than a handful of KPIs and this lack of focus in identifying the KPIs is an impediment to success. Data flows have to cut across functional domains with each process owner supporting his/her downstream partner to achieve their goals, while maximising data flow around his / her KPI. The BI team could start small, integrating enterprise data around KPIs, before bringing in ‘Big Data’ or unstructured data from outside the enterprise. For instance the procurement team must ensure that invoice data to be processed is well matched with the PO and the GRN (Goods Receipt Note) to enable the finance team which is usually responsible for making payments do their work in time. Besides, the BI team needs to ensure that the analytics team is equipped with the right tools to collate, analyse and disseminate data rapidly.

3. Build a team with the right skills
Once the BI strategy is aligned, the people strategy must be etched out clearly. Delivering outcomes in analytics typically requires three different skills in a variety of combinations. Technological capability to pull data together across the siloes using the BI infrastructure, adequate domain capabilities to understand the range and quality of data required right from master data management to monitoring transactional data and data science capability to build models to develop predictive and prescriptive insights.  The technology team  will also need to present the data smartly and quickly using the right visualisation methods while eliminating errors. Many enterprises fall short on the skill front, although they might have a smart BI infrastructure to support data collation.

4. Promote synergy
Analytics only delivers insights. For real outcomes, the business operations team must trust the insights and make relevant data-based decisions. And trust goes both ways. So, the analytics team must work with the business operations team to ensure that the right insights are provided consistently. Working together is key to achieving the required ROI from analytics investments.

5. Foster a culture of ownership
Given the need to drive analytics across the value chain, ownership and governance plays a key role in achieving success.  Senior level ownership to cut across siloes, strong governance to enable the different siloes in a process work together, and an understanding of the goals both upstream and downstream of processes managed ultimately decide the success or failure of the initiative.

- By Ravishankar Panchanathan, Head - Business Analytics and Reporting Practice, Infosys BPO

The thoughts and opinions shared here are of the author.