2019 saw a lot of activity within the field of data and analytics. Privacy regulations have been enforced across the world, organisations have steadily been migrating much of their data infrastructure to the cloud and AI has exploded, with companies taking steps to monopolise on the benefits that can be gleaned from Artificial Intelligence.
Broadly speaking, 2019 witnessed a shift in analytics. Extracting the meaning from data has moved away from being descriptive and diagnostic; the “what” and “how” toward the predictive and the prescriptive.
Data will be more important than ever across organisations. It will be owned by different entities across several different departments. This multi-faceted model requires speed and flexibility within an organisation alongside solid management and skill. In short, realising the potential of data requires a harmonious marriage of technology and company culture.
2020 will require a synergy between creating AI capabilities and ensuring a business is in the best place to exploit them.
Commentary has suggested that data and analytics looks set to be especially crucial for the retail and consumer products industries, with many predicting that there will be a fading of boundaries between the two. With competition at an all-time high, both industries rely upon smart and actionable insights to remain profitable and relevant.
What will this look like within the retail and consumer goods markets, and how will they create business value from data and analytics?
I have highlighted 12 trends which I can foresee disrupting these two areas of industry across 2020.
AI for Business:
AI implementation is rapidly occurring within organisations, with a focus upon practical cases which prioritise business need.
For consumer product companies, this is about assessing and predicting the efficacy of promotion campaigns and optimising personalised offers as well as harnessing the potential of increasing sales using mechanics. For retailers, this may include employee attrition assessments, preventing wastage or spoilage and dynamic pricing.
Enterprise knowledge graphs:
AI-powered enterprise knowledge graphs help put data into context and can demonstrate how different entities relate to one another. Such concepts allow organisations to uncover hidden
insights. For example, this may allow retailers to establish linked purchasing, suggest purchasing combinations or even suggest how an organisation could promote a product to a new market demographic.
Fading boundaries and data sharing:
From a data and analytics viewpoint, the boundaries within and between organisations are blurring. As such, different departments and organisations are having to share data. This data sharing is necessary to facilitate product development and overall product and service delivery. 2020 will see important steps being taken by the Consumer Goods Forum and other industry bodies that will support and drive such sharing and collaboration.
Synthetic data is any production data that applies to a given situation that is not obtained by direct measurement. The creation of such data is an involved process of data anonymisation. Synthetic data is a subset of anonymised data.
The use of this type of data is another option for data sharing across value chains. It can be used as an alternative if getting or creating data isn’t cost-effective or indeed possible at all. In these cases, synthetically creating data by complementing original datasets with similar alternatives may be enough to empower a machine learning algorithm.
2020 is likely to see Business Intelligence and analytics teams who formally worked independently from each other, coming together. These collaborative departments will work transversally within and across organisations to bring insight. More and more, they will focus on the predictive and prescriptive rather than the descriptive. This will require both a closer link to the business and the wider ecosystem of an organisation. Data science teams will become pivotal. They will leap from being a silent, supportive department to one that has a key business function.
Cloud migration will continue and intensify:
Organisations will continue their efforts to migrate their large data sets to the cloud. They will continue to adopt more cloud-native applications and retire legacy on-premises ones. This isn’t a new concept, per se. However, the rate looks set to accelerate significantly this year with more and more SaaS solutions becoming available to offer flexible, holistic, simple solutions of great business value.
Privacy and responsible AI by design:
Following privacy regulations being adopted across the globe, any technology solution that includes privacy-sensitive data will have to implement integral privacy regulation by design. Retail and consumer product organisations will have to be ready for this. They will be required to act transparently and with responsibility toward their customers and stakeholders.
More data from 5G:
Over 2020, 5G will become more commonplace. As such, the IoT uptake by consumer product companies and retailers will swell and will lead to more real-time data. The benefits of 5G are huge. For example, robots stocking shelves based on real-time data could eliminate the possibility of a product selling out, thus improving both customer satisfaction and profitability. 5G would also facilitate real-time delivery tracking, giving consumers the power to accurately track their purchase en-route.
Augmented data management:
As businesses become more data-driven, the amount of data-engineering work and analytics will need to increase. The highly skilled personnel to support this is both rare and costly. The answer is Augmented data management. This concept brings the power of AI to core data management tasks, leaving skilled technical staff to focus on higher-value work.
Data literacy and skills:
A data-driven organisation requires a data-driven mindset. Employees at all levels need to have the required data skills and be data literate. 2020 will see businesses have to invest in their people, and build the capabilities of their teams. These organisations will have to understand and trust the value data analysis can bring to their work. Success will depend upon both technological and cultural transformation.
Consumer relationships require data trust:
Consumer relationships will need to be built upon a foundation of trust. This will not just be driven by the regulatory constraints imposed across the world. Consumers are being equally critical and more informed. They have become more aware of how valuable their data is and are becoming more demanding concerning how it is being handled. This awareness is particularly relevant for companies who are developing D2C (Direct to Consumer) or subscription-based business services. Likewise, retailers who are offering alternative delivery models and personalised services are having to earn trust rather than presume it is being given.
Independent data managers:
As noted in the previous prediction, the focus is on the common need across all organisations to be trusted with consumer data. As a result, the future of personal data ownership is in flux. Consumers want transparency about how companies are using the data they share and a reward for sharing it! This emerging trend will give rise to a new breed of organisations and roles within them. Independent data managers will grow in both number and significance. A secure, compliant and trusted service will be required to enable companies to combat walled gardens and kill off cookies.
These predictions for 2020 all have one thing in common. The true value of data and its subsequent analysis can only be harnessed by organisations recognising the importance of data for business success. To realise the potential, organisations must be prepared to embrace the technology and its people must be prepared to support the change. One cannot happen without the other. The shift in mindset needs to be one of “future-ready”. Success relies upon a marriage of a flexible data infrastructure, a data-driven mindset and an investment in upskilling the workforce.
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