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Data & analytics trends for consumer products and retail companies in 2020

Dinand Tinholt
January 10, 2020

The beginning of a new year is a traditional time for resolutions and predictions. In the field of data and analytics, the past year has shown a great deal of activity, for example with organizations moving their data infrastructure to the cloud, with privacy regulation taking effect in different parts of the world, and with companies taking steps to realize benefits from artificial intelligence. Analytics (extracting meaning from data) is moving from being descriptive (what has happened) and diagnostic (why something happened) to predictive (what is likely to happen) and prescriptive (what to do for each predicted outcome).

What will be the key trends for 2020 and where will organizations focus their efforts this year? Based on conversations and ongoing work with clients, colleagues, and technology partners, below are some of the topics I think will be a priority for 2020 for consumer products and retail companies. I consciously group these two distinct markets together because one of the trends I foresee from a data and analytics perspective will be a fading of the boundaries between the two.

In our series on insights-based decisioning, we have highlighted that smart and actionable insights are needed for businesses to remain competitive and profitable. Data will continue to take on new importance across organizations and will be at different places and owned by different entities. This means that speed, flexibility and scalability are important but equally important are governance and skills within organizations. The key to AI is the partnership between machines and humans. Realizing the benefits from data is about both technology and culture; it is about enabling both the science of data as well as the art of it. For 2020 the focus will be on the interplay between creating AI capabilities as well as ensuring organizational exploitation.

Below are my 12 predictions for 2020 – one for every month. Over the course of this year, I will revisit some of these topics with tangible examples of implementations and the business value they create.

  1. AI: Beyond the Buzz and into business: Discussions about artificial intelligence and machine learning (often used synonymously although the latter is a subset of the former) are moving beyond the buzz and into the realization phase. Implementations of AI are increasingly taking place within organizations, focusing on use cases with clear prioritization based on business need. It is now all about delivering on the promise with tangible business value. For consumer product companies, this can for example be in assessing and predicting promotion effectiveness campaigns to allow real-time tracking and optimization of personalized offers as well as the opportunity to drive increased sales with mechanics beyond (margin-eroding) price cuts. For retailers different examples are employee attrition assessment and -prevention, reducing spoilage and dynamic pricing.
  2. The rise of enterprise knowledge graphs: AI-powered enterprise knowledge graphs put data in context and help understand how entities relate to each other. This is crucial for organizations in uncovering hidden insights. Such insights can relate to suggested combination purchases, event-driven purchases, or even how to promote products to “Generation Z” clients.
  3. Fading boundaries and data sharing: Organizational boundaries are fading – at least from a data and analytics perspective. Innovation of products and services takes place across actors in a value chain – either within an organization (across departments) or across organizations. Data sharing (which can be done in ways without actually giving away the data) will increasingly take place between organization to support product innovation and overall product and service delivery. Important steps are being taken by the Consumer Goods Forum, NRF, GS1 and OASIS and increasingly data sharing will not only support but actually drive such collaboration.
  4. Synthetic data: In addition to getting more data by sharing it across value chains, another option that will gain popularity is the creation and use of synthetic data. In cases where getting or creating more data simply isn’t feasible or cost effective, synthetically creating data by complementing original datasets with similar alternatives might be sufficient to empower a machine learning algorithm.
  5. Organizational agility: Business intelligence and analytics teams are transforming into broader data science hubs working transversally within an organization as well as across organizations to bring insights. Increasingly, their common focus will be on predictive and prescriptive insights as opposed to merely descriptive ones – requiring a closer link to the business and the broader ecosystem of an organization. Data science teams will become central to the organization and change from often being a support function to becoming a key business function.
  6. The migration of data infrastructures to cloud environments will continue and intensify. We will continue to implement large scale data migrations to the cloud, adopting more cloud-native applications and retiring older on-premises ones. While this isn’t so much a novel development from a technology perspective, the adoption rate will significantly accelerate this year whereby more and more Software-as-a-Service (SaaS) solutions offering flexibility, full service, simplicity, and value will be the preferred choice of organizations.
  7. Privacy and responsible AI by design: Privacy regulation has been adopted in different countries and will need to be implemented by design in any technology solution that includes privacy-sensitive data. Countries across the world have also set their goals on promoting ethical and responsible AI. It is important for organizations to adopt a principles-based approach leveraging existing global guidelines to be ready for this and also to act transparently and responsibly towards their customers and stakeholders.
  8. More data from 5G: As companies prepare for the advent of 5G, IoT uptake by consumer product companies and retailers will proliferate enormously and lead to more data and also more real-time This can be anything from robots stocking shelves in stores based on real-time data to reduce stockouts and increase customer satisfaction to consumers tracking real-time the en-route delivery of their packages. This requires that the data governance setup within an organization be robust and prepared for this.
  9. Augmented data management: As organizations become increasingly data driven, the amount of data-engineering and analysis work will increase. At the same time, highly skilled staff to support this is scarce and costly. Augmented data management brings the power of AI to core data management tasks (e.g., data quality management) to self-configure and self-tune certain processes so that technical staff can focus on higher-value work.
  10. Data literacy and skills: Becoming a data-driven organization means having a data- driven mindset. To realize this, it is crucial that employees at all levels in the organization have the appropriate data literacy and data skills. Organizations need to invest in building the capabilities among their people to understand and trust the value data can bring to their work. AI impacts entire organizations and ecosystems. This is a cultural transformation and as our research on this topic has shown, for many organizations cultural issues continue to hamper digital transformations.
  11. Consumer relationships require data trust: Consumer relationships will increasingly need to be built on principles of trust. This is not just driven by regulatory constraints across the world but equally so by critical consumers who understand the value of their own data and demand a choice in how it is handled. This is crucial for example for consumer product companies who are developing Direct to Consumer (D2C) or subscription-based business models and for retailers offering alternative delivery models and increased personalized services. Data trust must be earned and not presumed.
  12. Rise of independent data managers: The future of personal data ownership is in flux. As highlighted by the former prediction, the focus is on the common need across all organizations to be trusted with personal data. Companies are being asked to be open about how they use the data they collect. Consumers want payback for the personal data they share. This development will give rise to a new breed of organizations. Independent data managers will grow in significance. These organizations will provide a secure, compliant, trusted service to enable publisher/advertiser ecosystems to combat walled gardens and the (slow) death of cookies. Will this role be taken up by one of the current tech giants or will a new one emerge? I think the latter.

Whichever predictions come true this year, the key is for organizations to use their data and analytics assets to realize true transformation to support the business in innovating their products and services. It all starts with the business question that needs to be answered – these questions should be at the heart of all data initiatives. I believe the technology transformation and the organizational transformation of the data and analytics function within organizations need to go hand in hand. Organizations should invest in a future-ready flexible data infrastructure, internalize a data-driven mindset across all departments and invest in data skills for everyone in their organization.