Companies that have digitally transformed produce vast volumes of digital data that offer novel opportunities for data monetization. Build a data strategy to unlock new and eclectic use cases across business, customer, and IT operations. Download the new ebook to find out how.
Digital transformation is providing enterprises with an opportunity to deliver greater convenience to customers, consumers, and employees at a lower cost via automation and innovation. Companies are using the cloud to provide reimagined or totally new services that save time and money, reduce wastage, or improve accuracy.
What’s more, the technology and automated processes that drive this digital transformation leave a “digital exhaust” of data in real-time. This digital data is generated by the applications and processes that underpin these new services. It includes unstructured data such as video or sound, and machine data, which is generated by a device with no human interaction.
The emergence of digital data has led to a paradigm shift in the way data is valued, managed, and exploited. Monetizing it requires a combination of classic data disciplines with new capabilities. Digital data is quite different from traditional enterprise data, which has been output by CRM or ERP systems and is the foundation of enterprise data analytics.
Analyzing digital data can provide brand-new insight into all aspects of the organization, from customer relationships to business operations. Consider these use cases:
Improves sales analysis for retailers
Retailers know what consumers have bought or how much they spent by analyzing traditional enterprise data. However, they had no understanding of the events, habits, or behaviors over the lifetime of the relationship, or those leading up to and immediately following the sales transaction. This has changed with digital transformation. Retailers can now access the data from interactions created through mobile browsing web logs, chat logs, email, voice interactions, and social media postings.
This digital data represents the “missing” pre- or post-sales data. Now, analysis of the extended sales cycle is possible over days, weeks, or even months. The combined data points can further be used for decisioning and predictions when fed into propensity, predictive, or machine learning models to start stimulating customer transactions using digital marketing or location-based offers.
Planning for post-COVID return
Consider the very current topic of business contingency planning to restart operations during the COVID-19 pandemic. Enterprises can make use of sensor technology such as AI-enabled cameras, and Wi-Fi access points, to capture the presence of people, movement, and connected devices within a shop, warehouse, factory, or office.
The sensors can continuously stream digital data for cloud analytics to provide counts, dwell-times, movement, and location analytics within a space. This enables those organizations to demonstrate that they have implemented social distancing measures and are compliant to remain open and generate revenue.
Improving restaurant profitability
A well-known global restaurant chain has used data science to improve the performance and profitability of its restaurants. It developed a range of use cases including predicting restaurant performance, investment impact, investigation of price elasticity, identification of best new locations, franchise coaching, advertising contribution, optimization of service quality, and financial performance.
These use cases drew on a combination of enterprise data, digital data, and external data. The data was acquired, bought, and combined with internal data sources to achieve monetization. Results were impressive: one of the biggest restaurant sites managed to double revenues based on the results and changes it made.
Evolving data analytics
Digital data analytics builds on many of the practices that were developed for enterprise data over many years – even though the two forms of data are quite different.
Compared to digital data, enterprise data is far simpler in many respects: it is highly structured and almost exclusively lives within a relational database tied to its parent application. Enterprise data is always readable in Excel, accessible by SQL, and is usually made up of recognized attributes even if the metadata is not always accurate. It is slow-moving in nature, predictable, and relatively small in volume.
Over the years, enterprises have built up best practices in data analytics as the technology improved. These include integrating data from different systems, monitoring the quality of the data, and ensuring that master reference data across systems are harmonized. This had allowed them to design and build the most appropriate data models to support ever-more demanding questions.
The governance, ownership, and stewardship of the data have also evolved. While IT may manage the data infrastructure and processes, the business must take responsibility for the data use, quality, and availability. This, in turn, led to the chief data officer and business-led data analytics.
New challenges from digital data
Despite its maturity, many organizations continue to struggle with aspects of data analytics. Large organizations may have the resources but are too big and complex to manage all data uniformly. Data platforms and initiatives are often dispersed across business units or brands – with no single customer view across the organization and the possibility of overlapping or inconsistent data and terminology.
Mid-size organizations may have a better grip on the data due to smaller size and less complicated structure but may lack the resources or skills to exploit the data fully.
The arrival of digital data will exacerbate these issues. New challenges include lack of familiarity or understanding of digital data, questionable data quality, further integration headaches, and the sheer frequency and size of the new data arriving. Enterprises need to ensure that their enthusiasm around digital data does not make them forget the governance lessons they learned with enterprise data.
To help enterprises ensure that their data assets are not wasted, we have developed an ebook, that outlines nine steps to digital data analytics success. Download it here.
Article in collaboration with Julian Human, Digital & Data Lead UK, Orange Business Services
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