By: Mark Green
Next-generation data governance requires a next-generation operating model with new capabilities.
Data Governance is becoming a barrier to innovation. The central principles of Data Governance are to control what data gets used, how it gets used, and how it gets secured. This systemic control costs money. Data storage, handling, and processing needs to be decided on, documented, and locked down. And once done, the processes to consider change, adding to the costs of money and time. Then implementing that change costs even more money and time while adding change management risks as well.
As new varieties, volumes, and velocities of data escalate exponentially, pressure for new analytics and insights continues to increase disruption risks from market share to whole business platforms for companies that do not evolve fast enough. Companies need to increase their rate of innovation to stay competitive.
Intelligence automation with Blockchain in the middle provides the opportunity to manage data governance for the fast pace Digital Age. Done smartly, intelligence automation with blockchain can reduce costs, risks, and organizational friction while enabling companies to move much faster.
Data Governance is about registering data and every interaction, and then securing them so all deviations are pre-approved and audited. The current state catalogs these (static) and layers on a change management process (manual) to pre-validate and control all change. In even the most sophisticated companies we are familiar with, data governance tools tend to be stand-alone, not integrated tightly with the day-to-day use of data. Management of analytics is mostly in place but remains a business process without the ability to actively manage. The edge cases are departments supplementing systemic analytics with spreadsheet, where analytics are not locked down.
Current State Remains Rooted in the Past
Companies started data governance organically as a solution to quality risks. More recently, governments stepped in with reporting requirements like Sarbanes–Oxley Act, Basel I, Basel II, HIPAA to address accountability and protection risks. The convergence of these initiatives led to today’s data governance practices and associated industry of software solutions, consultancies, institutions, and academics.
This mature practice is now prime for disruption. The emergence of big data from an epidemic of monitoring devices is producing a flood of uncontrolled data, forcing companies to rethink Data Governance as well as the governance of the analytics and models that are increasingly running key components of our businesses.
The sharp debate today revolves around personal data. Europe is taking the lead by stipulating ownership and consent requirements with its General Data Protection Regulation (GDPR). Much closer to home, California is following. This comes after years of hacks. Random highlights include Target, Equifax, and the US Government. And there have been fumbles too, where systems were not hacked but where sloppy governance has led to massive misuse. Facebook provides a robust example. Their story starts with ignorance, they appear to be unaware how their clients’ data is used on their platform. Their CEO initially said “it is ridiculous” that anyone could think fake activity on Facebook could influence the US presidential election. Later, they disclosed that Cambridge Analytica accessed the personal data of 87 million US residents. It is still not clear what Facebook knew when, as their narrative keeps changing. Facebook maintained for much of this time that there was no data breach. The active debate points remain 1) who owns the data and 2) what does security mean?
Even after alignment on ownership and security, accountability, big data, cloud repositories, and interconnected activities are still evolving too fast to control. And pressure to act without governance will continue mounting as data volumes, their changing interconnectedness, and associated analytics increase.
McKinsey maps the level of change that data is driving by business over the past three years.
Change starts where information analysis matters most. But as sensors from the Internet of Things (IoT) become pervasive, disruption will spread.
Three anecdotes tell the story. Most companies currently only analyze 12% of the data they have. 90% of the world’s data has been created in the last two years alone. IoT will save consumers and businesses $1 trillion a year by 2022.
To govern with increasing volumes of data and continuously changing analytics, the whole of data governance must be automated. A new, unbreakable, automated way of registering and controlling the use of data and analytic assets is needed.
The digitalization of media is the easy place to find examples of these disruptions. The analytics in media relate to matching content with audience wants. The gravestones include many newspapers and magazines. Even digital companies like MySpace and Yahoo! got displaced by Facebook and Google for not moving fast enough. Amazon expands into industries on the back of analyzing and responding to more data faster than its competitors.
Healthcare and Financial services are prime for change as well. Companies with strong data governance tools and process in place are the innovation laggards. CDOs that focus on stewarding data instead of innovating through data and analytics will struggle. Strong central control often leads to small teams doing things on their own. The resulting lack of integration leads to inconsistent execution, costly manual spot checks of business analytics, confusion and increased risk. As the development and deployment of artificial intelligence with machine learning accelerate, the governance and risk management challenge compounds with static catalogs and manual processes.
Into the Future
The next-generation operational model has to register all data and algorithms systematically in an unchangeable form. The algorithms need to be registered by use case and oriented towards solutions, so the inputs and outputs in all processes that are used to provide solutions can be automatically audited as used. Then of course, the model needs to automatically keep a log of assets used for sequential tracking, including reports and who views. And this model and log have to be unbreakable, auditable, and transparent.
If all enterprise data and algorithms are automatically registered in a repository before using, that simplifies the challenge of controlling and tracking use over time. This repository provides both CDOs and business teams with a common array of pre-registered analytics for all data and analytic assets. If these assets are linked in a taxonomy that ties them to intelligence solutions on a runnable ecosystem, then analytics become easy to select, use, and track. Then if calls for requests for solutions are allowed, this ecosystem becomes organic and grows to fit the changing business conditions. Finally, if solutions to these requests are sourced in the open market, the ecosystem becomes an incubator of intelligence innovation too. This breaks the iron grip of today’s data governance while closing the gap between strategic need for governed data and the way data and analytics actually get executed by line of business teams that survive in changing times.
The Shortest Track Company is working with clients today to provide this “Intelligence-as-a-Service” ecosystem with its Merchandising Mart, where it handles registration, taxonomy, execution, requests for solutions, open market request sourcing, and usage tracking. Shortest Track puts blockchain in the middle to register, authenticate and log usage in an unbreakable ledger with an audit trail.
Re-platforming traditional data governance methods with blockchain becomes simple when the whole company has migrated to an automated registry and tracking model. Adding intelligence sourcing, selecting, and operationalization to the Mart makes use and innovation easy too.
Full automation and tracking of data and analytics allows companies to be nimble with data without losing control of how it is used.
Shortest Track is an Intelligence-as-a-Service solution company that manages and accelerates the intelligence supply chain by sourcing, selecting, operationalizing, and syndicating solutions. It is also at the forefront of Blockchain to re-platform data governance with a fully automated and scalable framework for securing data, processes, analytics, and solutions.