Staying at the forefront of Data-Driven Analytics
In recent years, data has emerged as the leading instrument amongst manufacturing businesses, especially CPG. This is due to the fast changing landscape of consumer preferences and influx of larger pool of 21st century competition, especially retailers.
However, generating large amounts of data on a daily basis does not always equal large amounts of insight. There are many companies who struggle to use the collected data efficiently and apply it to real-time decision making. Some are under the impression that one must invest in the latest database systems and sophisticated analytics tools in order to translate what they have into meaningful information. That may not always be the case.
We discovered this during one of our projects for a client of ours, who, despite being a leader in their industry, had significant challenges becoming analytically competitive. While they had established systems in terms of data collection from their key retailers, the reporting structure relied heavily on manual efforts. These efforts were from the people who have been with the company for ages, doing the same work, instead of using their valuable industry experience to build insights out of those reports.
Most companies start with basic reporting, then move onto predictive analytics before introducing higher level of analytical solutions such as prescriptive analytics, all in the midst of company growth. Each level must introduce process optimizations that builds on the last to increase efficiency and effectiveness. It is common for the process to get overwhelming during transition. However, a company must find a way to draw meaning from the increasing amount of data collected to stand as a leader, and not a follower, in any given industry.
Below, we have outlined various aspects of building a successful analytics practice to stay at the forefront of data-analytics:
There Is No Time Like The Present. – Utilize Data You Have
Many companies want to get their data right before entering the analytics space – we believe that if you wish to get where you want to go, start with what you have. Majority of organizations struggle to get the kind of quality data that they would want, however it should not halt the plunge into building strong analytics capabilities. Any data can be incrementally improved to reach the maturity level where strong models are built on most accurate, complete and recent data. However, it is a long term goal – so the earlier the start, the better are the chances.
Understanding what has to be done is far more important than how it has to be done. Many times, companies are not clear on the problems they want to solve and in which priority they want to tackle them. A few common questions we like to ask are:
- What are the top challenges you are facing today?
- Is profitability the problem or growth?
- Is customer attrition or customer inactivity the problem?
- How does the rest of the organization align to these challenges?
- How can analytics help achieve the goals long term?
Diving deep into the business problem can help uncover what you need to build the right analytics solution in order to draw the appropriate insights.
Identifying Quick Wins
Data analytics can be a critical success factor in many strategies, however there are also certain area in which analytics can prove a quick win. Identifying such areas periodically, alongside long term goals, can incrementally provide huge benefits to the business. These are quick to implement, easy to gain in user acceptance and generate decent return in short amounts of time. Preliminary business analysis is often the source for this.
Deciding on in-house or outsourcing
With the most of collecting and storing data falling while speed of analyzing data for insights increasing, many companies face the decision of building analytical capabilities in-house or leasing from specialized analytical service providers in the market.
Outsourcing is cost effective, quick in solution and readily available access to specialized talent and cross-industry experience. On the other hand, companies have concerns regarding data safety and IP protection when it comes to highly confidential data. It is important to arrive at a decision which aligns with your company goals to not lose time on building analytics capabilities and to remain competitive.
With stakeholders, 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. 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.
While operationally, building analytics capabilities can be a challenge, there is a bigger challenge is adapting to cultural shifts in problem solving and integrating data-driven solutions to people’s daily lives. This shift can often take more than a year, with long term benefits requiring long term efforts.
In conclusion, building strong analytics capabilities is no longer an option for consideration, it is a necessity to success in delivery to your customers and against the efforts of your competitors.
- “CPG Firms: Finding the New Analytics Normal” – Tibco
- “Commercial analytics insights CPG Companies” – Accenture
- “Big Data Talent Quandary: In-House Or Outsource?” – Information Week
- “Should You Outsource Analytics?” – Information Week