Fixing the “rubbish in, rubbish out” problem
When we hear talk about digitalisation and digital transformation, we often think of AI, machine learning, or business automation. However, for organisations to get the most out of these shiny new technologies, they need to ensure they have a solid data foundation which feeds these solutions with high-quality, trustworthy data.
So, why is good data important to digital transformation, and what role does Data Intelligence play in getting the most out of AI, Business Intelligence (BI) and Machine Learning solutions?
Rubbish in, rubbish out
The concept of “rubbish in, rubbish out” is an important one when looking at the data foundation of digitalisation. It’s the idea that AI and ML tools can only work as they are supposed to if they are fed the right data, and it’s a challenge that is becoming greater for organisations as we continue to create ever-increasing volumes of data.
Data collection is not slowing down any time soon. As data lakes swell to become data oceans, users struggle ever more to find data they can trust and use. Indeed, that challenge of finding quality data grows more thorny – and crucial.
Analytics may amplify the impact of bad data. Indeed, as businesses attempt to scale AI and BI programs, small issues around data quality can transform into massive challenges. Businesses talk a lot about how AI and BI can be used to transform how they operate, but if they want to achieve greater operating efficiency at scale, they need to use good data.
How can businesses achieve those assurances of good data? The answer is data intelligence.
What is data intelligence?
People in enterprises need access to quality, trusted data. Data intelligence is the answer. Simply put, data intelligence describes a system to deliver trustworthy, reliable data. Metadata (or data about that data) is a crucial piece in that puzzle, as it includes vital context about an asset’s past usage and history.
On a human level, data intelligence supports a holistic understanding of an asset’s past, present (and possible future!). It answers key questions, like who has used an asset, how they did so, and what they learned. In this way, data intelligence empowers human insights into data that stretch beyond its current usage. It guides how an asset might be intelligently leveraged in the future.
Zooming back from that single asset, data intelligence can unify a star system of isolated conclusions into a constellation of connected meanings.
It creates a platform that reflects philosophical questions about data in an organisation: Why do we collect data? What is our goal when we wield it? And how will we use data more intelligently, efficiently, and powerfully over time?
How data intelligence supports business growth
Analytics projects – which are growing in popularity and promise at enterprises everywhere – require high-quality data. Indeed, self-service analytics is only successful if people can self-serve data they can trust. Today, many leaders see data intelligence as the answer to the rubbish-in, rubbish out problem that’s long thwarted the success of AI and BI projects.
Metadata management, data quality and governance, and data curation are all examples of DI use cases. As for types, there are five popular and distinct types of Data Intelligence. All are designed to accelerate or improve business processes, albeit by distinct methods.
Descriptive and prescriptive intelligence boast the longest history. Descriptive data intelligence analyses and reviews data to inspect its overall performance. Prescriptive data intelligence is diagnostic. It analyses data to form concomitant knowledge and offers counsel on how to better the operations around data.
Diagnostic and predictive data intelligence historically came next. Whilst diagnostic data intelligence analyses the cause for an occurrence in an attempt to explain why something unfolded in a particular way, predictive data intelligence considers all of the historical data available in order to forecast and predict the future. Finally, decisive data intelligence measures the value of data to suggest new, alternative courses of action.
In the past, such data intelligence use cases were applied in pursuit of competitive strategy, to the external world of business machinations. Active metadata has changed that, and pushed the application of data intelligence inward, as well. Active metadata learns from how people use data within an organisation. Those insights can be leveraged to improve operational efficiencies, too.
For too long, AI, ML, and BI solutions have failed. Data intelligence is the answer to their future success. Today, thousands of companies – and even more of their employees – are drowning in bad data, and struggling to fix the “rubbish-in, rubbish out” problem. Data Intelligence is the answer and future face of successful analytics endeavours.