Data & Analytics Solutions Guide 2018
In this edition of the Technology Solutions Guide series, CGT presents a comparison chart of solution providers on the forefront of data and analytics. To kick things off, a roundtable with experts from AnswerRocket and SAS provide thought leadership on navigating the challenges and opportunities involved in effective, efficient data management and analytics.
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CGT: It’s now generally understood that a strong business analytics practice is mandatory for future success in the consumer goods industry. What obstacles are still in the way of achieving this for some companies?
FINLEY: The greatest obstacle may be that experts who know the consumer goods market are largely not the same individuals with analytics and modeling skills. Businesses that want to invest in big data and data science need to transfer the knowledge of brand experts and category managers into the lines of code and training sets that are the new currency of artificial intelligence. The results of analytics and AI will be nonsense without a collaborative partnership between the old and new ways of doing things. Even if the results are correct, it will take trust and confidence to use them to drive the business.
MITCHELL: Many consumer packaged goods companies trying to monetize data are starting to cultivate the analytics culture at the same time. Developing a strong business analytics practice requires stitching together the data you have today while developing new data sets to better understand cross-channel demand and local customer needs. Often, the responsibility for data is diffused across departments and budgets. By 2021, in 75% of large enterprises, the office of the “Chief Data Officer” will be seen as a mission-critical function comparable to IT, business operations, HR and finance.
Despite recent advancements in data management technology and tools, many teams are overwhelmed by today’s massive volumes and the variety of fast-moving data. By providing access to all as a shared service, seamless data access, integration, quality governance and data federation companies can greatly accelerate analytics adoption.
CGT: It’s also often suggested that analytics can be the fuel that drives greater alignment across the enterprise. How might this take place?
FINLEY: If corporate strategy from the top is one bookend, data is the other. Without data, departments are free to interpret the corporate strategy and take it in the direction that best suits their objectives. But data is the one source of truth that all departments can use to reality-check results and fine-tune their contribution to the strategy. When universally accessible and up-to-date, data can provide the guardrails that allow a business to move quickly, even while adjusting the many levers needed to compete in real time.
However, data requires governance. Otherwise, teams will pull data from convenient sources or interpret it differently, with the purpose of promoting a certain point of view. So it’s key to get agreement on key metrics and originating sources.
MITCHELL: Although organizations have used analytics for decades, it has been mostly limited to specialists trained in math, statistics, econometrics, etc. That’s no longer the case. As data exploded, so did technologies that enabled a wide range of users to access, analyze and find value within it. Fueled even further by advances in connectivity, the cloud and computing power, analytics now feeds on huge amounts of data to produce insights. These advancements are creating an economy where data, people and machines must work together to stay competitive and accelerate customer experience innovation. This really starts at the individual level by enabling anyone who is curious about data to quickly and easily explore and share gained insights, driving new experiences and operating models — truly democratizing analytics for all.
CGT: What role will artificial intelligence play in the pursuit of analytics excellence? How long before the consumer goods industry is largely “powered by AI”?
FINLEY: In order to power the consumer goods industry, machines have to recognize patterns in CG data. These patterns will come in many shapes and sizes, such as market conditions where pricing can be optimized or weather conditions that will lead to out-of-stocks. Brand experts and category managers make hundreds of nuanced decisions based on patterns, so AI has a lot of catching up to do. But before 2020, the consumer goods industry will see automation for key drivers like pricing and promotion, at least in the simplest cases.
Similarly, legacy concepts like elasticity will be replaced with predictive real-time models that juggle time, location, competition, and many other factors simultaneously. Within 10 years, CG experts will be guiding and confirming the recommendations made by AI solutions in a majority of cases.
MITCHELL: Advances in AI over the past decade have been supported by supervised deep learning by training machine learning algorithms to perform narrow, single-domain tasks. We’re now seeing more unsupervised learning systems that learn faster, require less data and tackle broader, more complex problems. These supervised and unsupervised learning systems can be used for intelligent automation that can help retool existing business processes.
With more data and application integration, the variety of business challenges to which AI can be applied is expanding and letting non-specialists automate repetitive day-today work activities with more accurate, real-time decisions. Many CPG leaders are still struggling to recognize the value that AI can deliver and how it can create tangible ROI. However, they are very open to creative ways AI can generate value. It is expected that companies will continue to seek opportunities to adopt and implement AI technologies by extending the value to predictive and prescriptive modeling.
To download the full report, including a comparison chart of 33 solution providers on the forefront of data and analytics, click on the link below.