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Companies of all shapes and sizes are increasingly realizing the need to continually improve their competitive diversity and avoid falling behind the world’s digital-native FAANGs – data companies like Google and Amazon – the first companies to take advantage of data to dominate their markets. In addition, the global epidemic has galvanized digital agendas, data and agile decision-making for strategic priorities spread across remote areas. In fact, a study by Gartner’s Board of Directors found that 69% of organizations led their organization to accelerate data and digital business initiatives in order to accelerate Covid-19.
Migrating data to the cloud is nothing new, but many people will see that cloud migration alone will not magically transform their business into the next Google or Amazon.
And most companies find that once they migrate, the latest cloud data warehouse, lakehouse, fabric or mesh doesn’t help them use their data power. A recent TDWI research study of 244 companies using Cloud Data Warehouse / Lake found that an astonishing 76% experienced most or all of the same challenges.
Cloud Lake or Warehouse solves only one problem – providing access to data – which, although necessary, is not solved for the usability of the data and certainly not on a specific basis (which gives FAANG their ‘byte’)!
Data utility is truly the key to enabling digital businesses – which can use data to hyper-personalize every product and service and create unique user experiences for each customer.
The path to data utility
Data is difficult to use. You have raw bits of information filled with errors, duplicate information, incompatible formats and variability, and sealed disperse systems.
Moving data to the cloud eliminates these issues. TDWI reports that 76% of companies have confirmed similar on-premises challenges. They may have moved their data in one place, but it is still surrounded by similar problems. Same wine, new bottle.
The ever-increasing bits of data need to be finally standardized, cleaned, linked and adjusted for usability. And to ensure scalability and accuracy, it should be done automatically.
Only then can companies begin to uncover gems hidden in the data, new business ideas and interesting relationships. Doing so allows companies to gain a deeper, clearer and richer understanding of their customers, the supply chain, the processes, and to transform them into monetization opportunities.
The objective is to establish a unit of Central Intelligence, centered around data assets – monetary and easily usable levels of data from which the enterprise can derive value on demand.
Given the current constraints, it’s easier said than done: extremely manual, abbreviated, and complex data preparation implementation – meaning not enough talent, time, or (right) tools to handle the scale needed to prepare data for digital.
When a business is not running in ‘batch mode’ and data scientists’ algorithms are predictable on continuous access to data, how can current data preparation solutions cut down on routines that run once a month? Isn’t there a digital promise to make every company omnipresent anytime, anywhere?
In addition, few organizations have enough data scientists to do that. Research by QuantHub shows that data scientist job postings are three times the opposite of job search, which currently leaves a gap of 250,000 unfinished positions.
Companies facing the dual challenge of data scale and talent shortage need radically new approaches to achieve data utility. To use the parity of the auto industry, as BEVs have revolutionized how we get from point A to B, advanced data utility systems will revolutionize the ability to create useful data for every business to become truly digital.
Solve utility puzzles with automation
Most people see AI as a solution to analytics decisions, although FAANG’s greatest invention was the use of AI to automate data preparation, organization, and monetization.
AI should be applied to the tasks required to solve for data utility – to simplify, streamline and supercharge many of the tasks required to create, run and maintain useful data.
The best approaches simplify the process in three steps: ingest, enrich, and distribute. For ingest, algorithms coral data from all sources and systems on speed and scale. Second, many of these floating bits are linked, assigned and fused to allow for instant use. This useful data must then be configured to allow for flow and distribution across customer, business and enterprise systems and processes.
Such an automated, scaled and all-in-one data usability system frees data scientists, business experts and technology developers from tedious, manual and delicate data preparation while offering flexibility and speed as business needs change.
Most importantly, the system allows you to understand, use, and monetize every last piece of data on a specific scale, enabling digital business to rival (or even beat) FAANG.
After all, this is not to say that cloud data warehouses, lakes, textiles or whatever the next hot trend will be is bad. They solve for a much needed purpose – easy access to data. But the journey of digital does not end in the cloud. Data utility on a scale will put the organization on the path to becoming a truly data-first digital business.
Abhishek Mehta is the Chairman and CEO of Tresata
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