At a recent conference, I was struck by an admission of a data leader at a top 10 consumer goods company about the poor state of data in CPG. “Nobody has figured out master data in CPG,” was the message that leader conveyed to us. They may have figured out a slice of their data but not a holistic approach across all types of data. We are still in the early stages.
Understanding the historical context is important to explaining this gap. CPGs have been fundamentally focused on serving their retail partners for decades, delivering products on time and in full, driving insights through advanced analytics on baseline sales and promotional events, continuously looking for win-win opportunities. External analytics have taken precedence over holistic internal analytics over time. But now, CPG companies are seeing opportunities in expanding into new digital channels to communicate directly to the consumer and also using DTC to sell directly. Only 40% of consumer goods companies that have made digital and analytical investments are generating adequate return on investments. Better data is needed, because more associates are expected to do analytics in a more democratised analytics approach.
Data analytics have expanded scope
For decades, CPGs have been primarily focused on the distribution points of their analytics conducting analysis on business performance, market analysis, new product launch analytics and promotional/pricing analytics. While those are still important, the expansion of the business operations of CPGs and their new customer facing digital operations create means that 1) a much wider variety of data types are being analysed and 2) a greater variety and number of people are working on the data and require solid quality and fit for purpose data.
Needs going forward
CPGs are increasingly needing to be more agile to support customer analytics teams as well as enterprise analytic needs. This requires a greater reliance on automation and streamlining of standard processes. Successfully filling this agility requirement can help CPGs continue to exceed the needs of their retail partners as well as keep or retain an edge on digitally native upstart brands in the digital marketplace. While data scientists have filled the gaps in bringing in various data sources to drive high level analytics and most companies have Business Intelligence (BI) dashboards, underneath the beautiful visualisations you’ll find complex sequences of data transformations and other data band-aids to get the data ready.
Building a better foundation for agility in analytics
There are concrete steps CPG companies can take to build a better data foundation:
Step one: Review how data is used today
Understand what is being done to connect and integrate data across divisions, geographies, etc. Identify the biggest time wasters in terms of data manipulation from analytics teams that are being repeated time and time again.
Step two: Integrate data
Develop and execute a plan to develop a single source of truth for your most valuable data, such as supplier, B2B retail customers, end consumers, products and more. Most CPGs have rightfully started with product data, but as analytics needs and expectations increase, weaker data types can impair the data quality when merging data for analytics.
Step three: Improve process and governance
Opportunities favour trustworthy and responsive consumer brands companies. By improving the process of acquiring, creating, enhancing and approving data, there is increased trust, because there is transparency in the process. With a master data management platform, users can have visibility into the origins of the data and how it was transformed over time.
Step four: Scale into the future with multi–domain master data management
Most CPGs have a minimum of six different classes of data, each potentially of high quality but also siloed data structures. By using multi-domain master data management instead, relationships can be created between the different data types and a unified approach and governance can be applied. With this in place, teams can configure these relationships and adjust over time as new data is brought in or the business changes or grows in the future. Building these relationships in master data help democratise the core business data, allowing the data scientists to work on higher value projects.
Empower your team and improve your business with better data
Many CPGs are looking at ways to implement their vision of improved end-to-end data management and analytics. Two years ago, this may have meant having the right visualisation software, building an analytics team to wrangle the data and build models – but this is unsustainable, as data variety and volume and complexity have increased. Building a trustworthy source of data, a digital business hub, not only helps your business but will also make it easier for your talented employees to access the data, empowering them to make valuable contributions.