Founder & Product Manager @ Euryka Data Management Inc.
June 21, 202
The act of using data to increase revenue is called monetizing data. There is a lot of money to be made in data and effective data management is key to maintaining market share and increasing profitability. It is no longer sufficient to focus just on data governance; data management teams need to broaden their scope to focus on making the data available for monetization. In the quest to monetizing data, organizations are heavily investing in AI, machine learning, digital transformation, data platforms, data lakes, data catalogs, analytics, etc. Here are some statistics from Gartner to demonstrate the investments organizations are making in technology:
- By 2022, 35% of large organizations will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020 
- By 2022, public cloud services will be essential for 90% of data and analytics innovation 
- By 2023, graph technologies will facilitate rapid contextualization for decision making in 30% of organizations worldwide 
- By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures 
These statistics speak to the emerging prominence of data as a commodity. But the reality is Data management practices in most organizations are not effective and they struggle to make their investments realities. Here are some sobering statistics from Gartner to demonstrate this argument:
- 85% of big data projects fail 
- 87% of data science projects never make it to production 
- Through 2022, only 20% of analytic insights will deliver business outcomes 
There are many reasons for the failures such as lack of shareholder support, lack of metadata, complexity, data quality, etc. If all these can be summed up into a single theme, one can conclude these failures are due to a lack of intelligence. Executives need to understand that “becoming and staying data-driven is a process and continuing journey, seldom a destination” . The technology investment and talented staff are great, but one must always remember information is not intelligence.
Collins’s dictionary describes information as “someone or something consists of facts about them” . And intelligence is defined as “the ability to think, reason, and understand instead of doing things automatically or by instinct” . For example, I can Google some information about astronomy to impress my friends at a party. I am not an Astronomer and do not have any educational background in astronomy. But, after doing some Google research, I can recite some information on this topic. This does not make me intelligent or knowledgeable in astronomy. The same is true in data management. Creating teams who are knowledgeable in their business with the ability to make intelligent decisions is a continuing journey that goes well beyond technical architecture.
Most organizations invest very little in creating processes and educational initiatives that create a data-driven culture. Often, the focus and budget are on the latest and greatest tooling and digital transformations, consequently, the teams end up focusing on learning systems rather than gaining intelligence about their data from a business perspective. This lack of business understanding creates a big divide between IT and their business teams. Along comes the Chief Data Office (CDO) to bridge this gap. The CDO is supposed to create business value by managing all aspects of data; the main objectives of the CDO are:
- Bringing IT and business together to deliver projects and monetize the data
- Governing data so it is protected and controlled while data quality is improved
- Enhancing data literacy
- Implementing advanced analytics
In reality, CDO teams focus heavily on data governance and end up creating committees, checkpoints, and policies that aim to police the development and project delivery teams rather than creating data intelligence across the teams.
Becoming a data intelligent organization means looking for opportunities to educate the teams on data and creating an environment that encourages that learning process. Access to education does cost money, time, and resources but this is an investment that goes a long way in elevating data intelligence in an organization. Here are some thoughts on how organizations can increase intelligence amongst their analysts, architects, and managers:
- Accessible metadata that is actively used as part of project delivery encourages teams to think critically about source data elements, their meaning, and how they map to another system. The documentation and discussions generated to contribute to intelligent teams and well-inspected project delivery.
- Self-serve training material that is segmented into short videos is cost-effective and creates a continuous learning environment. Teams can access the videos as they need them or proactively learn more about their business.
- Create a team of teachers and shepherds who guide project delivery teams to help access the correct resources such as data flow diagrams, subject matter experts, decision-makers, architecture blueprints, and metadata. It is important to note this team of teachers and shepherds do not police the projects rather, they are a group of knowledgeable people who aim to increase the intelligence of the organization by providing helpful insights and information.
- Proof of concepts (PoC) allows organizations to learn and test a hypothesis without committing it. A PoC does not cost too much money and is the best way to try, test, and find the best technology or strategy, or process.
- Lunch and learn sessions and hackathons that focus on a topic or technology are effective ways to encourage the employees to keep learning. It is important to get subject matter experts to conduct the session, so it is informative and insightful.
Collecting information is important but creating a culture where that information can be used to make intelligent decisions is even more important. In today’s data world, CDO teams focus on data governance that burdens delivery teams with bureaucratic processes that make project delivery and innovation sluggish. Also, IT teams focus on digital transformations encouraging team members to learn systems rather than understand the business. Intelligent organizations avoid these pitfalls and create solutions to become market leaders and monetize their data. It is not enough to hire talented people; organizations need to implement continuous learning opportunities for employees to advance their business knowledge, so they contribute to intelligent decision-making.