The state of pre-pandemic data and technology
Prior to 2020, it was hard to find an example of a truly digital company focused on transforming analytics and streamlining decision-making across all business functions.
In 2017, NielsenIQ segmented European FMCG clients on a “maturity” spectrum and identified four segments:
- Tech visionaries: 12% of companies that invest heavily in their own technology and data lake strategies.
- Tech hopefuls: 23% of firms acknowledge technology as an important enabler and differentiator but they’re looking for guidance on how to adopt.
- Tech for purpose: 34% of companies did not include technology as a major focus. They instead focused on growing their existing business models and used technology for specific use cases.
- Tech laggards: 31% of firms don’t view technology as a critical differentiator. They might adopt if pushed but overall have low willingness to spend.
Before the pandemic, the tech visionaries were on a two- to three-year journey to realize their insights-driven goals. Many were course-correcting by pivoting from isolated IT-lead data and tech initiatives or DIY strategies and redefining their end goals. Many had underestimated the level of effort and costs involved.
The FMCG industry watched and waited for early adopters to write the new data and technology playbook. As a result, conventional ways of working were deemed good enough for the majority of FMCG companies, and the pace of technology adoption remained slow compared to other sectors. Time-strapped data experts supported the day-to-day data needs of entire businesses, with little time to take analytics to new levels. The industry spent approximately 80%* of analytical time on low-value tasks like data structuring, scraping, and cleaning.
With few data experts supporting many users and use cases, “data silos” were the norm. Companies focused on efficiencies within the status quo, rather than a growth agenda.
This reticence to embrace technology was surprising given the industry’s financial performance. In the pre-pandemic decade, the FMCG sector underperformed based on the average economic profit by more than 13%. The need for radical change was becoming harder to ignore.
Advent of COVID-19 and 2020
In March of 2020, conventional ways of working with data and technology were upended by the pandemic.
Business leaders and cross-functional teams needed collaborative data that would help them make faster decisions, often in the absence of data experts. Senior executives and casual data users needed decision-ready data and analytics. Analysts’ workloads increased to deliver on financial commitments. Broader, faster data usage, delivered in the most efficient manner was essential.
Retailers were resetting their spaces and rationalizing with urgent range reviews and pushing for more everyday low prices, putting more pressure on time-strapped analysts against the inevitable need for retailers to pass on their higher costs to FMCG manufacturers to be COVID-19 safe.
Gartner reported in September 2020 that 7 out of 10 boards accelerated their digital transformation due to COVID-19. The pace of technology adoption was unprecedented. Data and analytics went from “nice to haves” to necessities.
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2021 common pitfalls and considerations
In 2021, as some places around the world moved out of restrictive living conditions, FMCG business leaders emerged with new mindsets toward data and technology. After seeing the benefits and necessity of both first hand, leadership teams across the industry accelerated enterprise-wide digitization plans for data and analytics.
With new technology, companies could remove silos and make faster decisions across business functions, a necessity in the new normal. Technology investments, once perceived as prime areas for cost-cutting for the years ahead, became investments in growth agendas and opportunities to create new data cultures.
Ingredients of a data-driven culture
There are many conversations about the best way to become an insights-driven organization. Here’s what companies tell us is making a difference as they lay data foundations for the next 12 to 24 months.
- Lead by example: It’s important for leadership to demonstrate new ways of working, cite early successes, and constantly provide clarity on the opportunity, culture shifts, and end goal. Managing directors can act as sponsors for accelerated transformation programs that emphasize the new strategic imperative.
- Unified and improved collaboration between IT and Analytics teams: There’s been a shift from isolated, IT-led data transformations to collaborative efforts between IT and analytics leaders. Together, they can realize the scale of the transformation and new opportunities faster than before. It was not uncommon for an IT-led initiative to stall or fail because of a lack of data governance expertise. Analysts sometimes struggled to “democratize” or widely share their methods. When their expertise is combined, IT and insights teams can accurately estimate the level of effort and cost involved in data governance, be bolder in use case selection, and scale data and analytics programs faster.
- Simplify for all: Streamline data processes and have a holistic, approach to data, analytics, and technology for users. Segment end-user based on data literacy needs—remember, it’s not one size fits all. It can and has to be easier for management, data novices and analysts to deliver sustainable change. With the right technology, new starters can pick up the seasonal analytics drain after two hours of on-demand training. Brand teams can review global performance in ten minutes and see their competitive threats without an insights expert. Data experts can find white space in adjacent categories in thirty minutes. If the technology is too sophisticated for the masses, no one will adopt. Simplification based on realistic end-user need will have a bigger impact.
- Adopt a data and analytics guide: Make it easier for all business leaders (not just analysts) to uncover the most granular details to inspire big picture ideas, envisage the future and take decisive actions. Create self-sufficiency with accommodations like digital training, multi-language chat box support, and guided analytics, but be realistic on what support cross functional teams need. Uncover what you really need for your current and future data literacy requirements.
- Hybrid versus DIY approaches: The race is on, but what is the most effective and efficient way to win? What third-party data systems can you leverage today versus build yourself in-house? Take a hybrid approach today and be ready for tomorrow.
In 2022, expanding ad hoc analytics capabilities and scaling across all business functions will solidify data as an enterprise asset, help drive more sales, and increase relevance with vendors. Industrializing analytics across mainstream business functions, adopting machine learning and bigger data sets to streamline decision making is no longer optional—it’s business critical. But it doesn’t have to be daunting. The right tech and tools can enhance your data, and data cultures, by empowering teams to make tactical and strategic decisions based on smart analytics—the new gold standard.