Fractal Analytics Blog

What Shapes Your Analytics Maturity Pathway

What Shapes Your Analytics Maturity Pathway

Jay Mehta
By Jay Mehta
January 7, 2016

Data is the latest weapon in the market that is deciding the leader in almost all the industries, especially CPG. The key reason being, the fast changing consumer preferences and influx of larger pool of 21st century competition, primarily the retailers – both, brick & mortar and online.

However, in spite of this gigantic pile of data generated every single day, the plight of CPG companies is similar to a soldier who is given a latest automatic machine gun but doesn’t know how to effectively use it. There are companies who just don’t know what to do with the data. Those who know, are, perhaps, not using it efficiently.

Very few companies are able to juice out real valuable insights out of the data and influence the decision making in almost real time. Many of us tend to think that only large corporations with tremendous financial muscle to invest in latest database systems, sophisticated analytics tools and recruit crème-de-la-crème analytics talent are able to translate this data into meaningful information. However, that may not be always true.

For example, during one of the projects for a large CPG corporation we had a series of discussions with the client to understand the kind of analytical challenges they were facing. Based on these discussions, we realized the long and painful path they had to cover in order to become analytically competitive, in spite of being among the leaders in their industry. While they had established systems in terms of data collection from their key retailers, the reporting structure heavily relied on manual efforts. These efforts were from the people who have been with the company since ages and doing the same work instead of using their valuable industry experience to build insights out of those reports.

Companies, generally, start with basic reporting to predictive analytics and from there move/expand to higher level of analytically matured solution like more prescriptive analytics such as building optimization and simulation solutions. At each level as well, there are process optimizations that happen to make it more efficient and effective. For example, Starbucks reduced the number of reports from 300+ to 11 KPI reports that served their managers in evaluating and managing the performance of their stores. Such optimization, eventually, also helps in increasing adoption as well as inviting suggestions/feedback that will make the reports more relevant.

Through numerous studies and research, it has become obvious that incorporating big data and analytics into the business strategy as well as operations is key differentiator between the leaders and the followers in a given industry. There are various aspects of building a successful analytics practice which can make or break the analytics maturity pathway.

Don’t Wait for All Data, Start Playing:

In case you are thinking that I will get my data all correct before I enter into the analytical space, it will never help you reach where you want to. Getting gold standard data is a distant dream for many organizations, including the ones who have pioneered analytics or are almost there.

Majority of the organizations still struggle to get the kind of data quality they would want. There many challenges to it like differences between data collection agencies in terms of coverage, granularity, periodicity, methodology, extrapolation techniques and certain business rules that they apply. In addition, there are manual in interventions at various points of data reporting which increases disparity between the data sources. However, that should not stop the plunge into building strong analytical capabilities. Many organizations start with whatever they have. Both the tracks, data and analytics, are incrementally improved to reach the maturity level where strong models are built on most accurate, complete and recent data. However, it takes years of concentrated efforts, which is why it is important to start early.

Setting Priorities:

Understanding what is to be done is far more important than how it is to be done. Many times, companies are not clear with what problems they want to solve and in what priority.

  • What are the top challenges you are facing today?
  • Is profitability the problem or growth?
  • Is customer attrition the problem or customer inactivity?
  • How does the rest of the organization align to these challenges?
  • How can analytics help achieve the goals for the coming years?

A couple of times, I have worked with clients who realize at the end that they are solving the wrong problem and they want to look at something else. It is not drastically different from what has been done but little extra effort in fine tuning the business problem can help you save some time in building the right analytics solution.

Identifying Low Hanging Fruits:

While there would be strategic areas of importance you will have in mind where analytics would be a critical success factor, there would also be certain areas where analytics can provide quick wins. Identifying such areas periodically can incrementally provide huge benefits to the business. These are quick to implement, easy to gain user acceptance and generate decent or good returns in short period of time. Preliminary business analysis is often the source of such low hanging fruits.

In-house vs Outsourcing:

This is one of the hottest board room discussion topics these days. With cost of collecting and storing data falling every day and speed of analyzing that data and extracting valuable information increasing, companies are contemplating whether to build analytical capabilities in-house or lease it from specialized analytical services providers in the market.

Outsourcing brings in benefits such as cost advantages, speed to solution, ready access to specialized talent and cross-industry experience. On the other hand, companies have concerns regarding data safety and IP protection that prevents them from trusting a third party with their highly confidential data. Thus, it becomes very tricky, at times, to quickly arrive at a decision to whether to outsource analytics or build it in-house.

One of the common challenges, companies new to the concept of analytics face is lack of knowledge of the kind of people they should recruit to build a strong analytics teams. Talent scarcity in the area of analytics, add to the complexity. With highly dynamic market in terms of consumer preferences, companies can hardly afford to lose time on building analytics capabilities, be it in-house or outsourced, which can help them stay competitive.

Manage Stakeholders:

Stakeholders, especially the end user, buy-in is critical for success of analytical endeavors. Active participation and inputs from end users ensure higher adoption and a wider scope of scalability of the solution. However, political scenario is also an important consideration because many times there is conflict of interests with respect to adoption of analytical solutions. For example, a consumer risk model deployed for an insurance company means limited ground for sales folks to cover which directly impacts their commissions and also holds them accountable for any risky customer they sign up in spite of being aware of the risk.

Analytics will often reveal trends which are in contrast with conventional gut feel. People, by nature, hate to be proven wrong. Hence, besides just focusing on taking the end users into confidence, understanding “how” to take them into confidence is also very important. Strong senior management (C-level suite) involvement and buy-in from the early stages itself helps create necessary influence on the end users as well. However, convincing is always better than influencing.

Culture Shift:

While operationally, building analytics capabilities is definitely a challenge, the greater challenge is to build an organizational culture that not just adopts data driven solutions but seeks it as a part of people’s daily work life. This requires a significant culture shift in terms of approaching problem solving, analyzing it and taking decisions, which can take more than a year. However, long term benefits require long term efforts as well.

Building strong analytical capabilities, in-house or outsourced, is moving from a luxury to necessity. Strong data driven decision making culture is, and will be, one of the key differentiators between successful and not-so-successful companies in the marketplace.

References:

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About the author:

Jay is a Senior Consultant in Fractal Analytics with 6+ years of experience spanning pharmaceuticals, banking, insurance and CPG industries. His key areas of experience include dashboarding and behavioral scorecard development. He has a dual degree MBA from ESCP Europe and MDI Gurgaon. 

 

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