Each of these three capabilities is underpinned by technology, processes, skills, and a culture that values data literacy, incorporates machine learning, and provides business intelligence plus data intelligence or intelligence about the data itself, to enable the rest of the data to decisions life cycle. Specifically:
- Machine learning (or if you prefer AI) not as an end goal of some type of general artificial intelligence, but as a means to use probabilistic techniques (combined with deterministic rules) to help automate tasks and activities of data engineers, analysts, and business users, such as:
- data ingestion, cataloging, quality, and integration, etc.
- root cause analysis, scenario evaluation, key driver identification, etc.
- The synthesis of information must be based on much higher data utilization rates. As more data gets captured and becomes available from external sources, the ability to use more of it becomes a differentiating factor. That includes taking lessons from industries other than your own. For example, the investment community has been active in using so called 'alternative data,' which refers to data not coming from typical financial statements. However, as we often tell our clients, 'alternative data is simply data you haven't used before.' Every organization needs to both incorporate more external data and expand their utilization of internal data to improve their enterprise intelligence.
- Non-obvious relationships analysis (which can't be identified from pre-built reports) will take on new urgency and is being driven by the need to embed decision support functionality across all applications, to reach not only executive decision makers, but all knowledge and front-line workers, and increasingly machines.
- Data intelligence will become another tool in the quest for the future of intelligence. It will allow enterprises to answer questions about who is using the data, who created the data or asset, and who is responsible for it? What does the data represent, what is the data being used for? What relationships are inherent within the data and between the people (and machines) that are generating and consuming the data? When was the data created, when is the data being used, and when will the data expire? Where is the data in the organization, and where is it being consumed? Why does the data exist, why is the data being persisted, and why is it being used? How was the data created or captured, and how is it being used?
The imperative to change data and analytics processes and practices has never been clearer. Faster innovation cycles and greater volatility don't require simply more data or more siloed information. They require a contextual decision environment. As Nobel laureate Herbert Simon said, 'In an information-rich world, the wealth of information [....] creates a poverty of attention [...].'
Finally, the imperative to rethink what it takes to increase enterprise intelligence and to compete in our digital economy applies to everyone. It is not a domain of only a few unicorns - success cases can be found across industries, geographies, and among start-ups and companies that have delivered value for decades. The question is whether you'll embrace this future of intelligence or be left behind.