Data volumes and computational processing power are growing at an unprecedented speed and scale, with no signs of slowing. Scientists are using Yotabytes (1024) to describe how much government data the FBI have, and soon the Brontobyte (1027) will be used to measure the sensory data generated by the internet of things. In an attempt to visualize the magnitude of these measurements, a Yotabyte is the equivalent of ~250 Trillion DVDs (EDM Council). Megabytes and gigabytes are becoming an increasingly irrelevant measurement in the world of ‘big data’.
The problem at an enterprise level, is that businesses are not coping with the exponential growth in data volumes. In fact 55% of all data collected by companies goes unused. Given that 54% of leaders say the ability to analyze data would improve decision-making and 87% of global senior managers & executives link better decision-making with improved financial performance, the case for understanding a firm’s current data management capabilities is no longer just a ‘nice-to-have’. The most accurate and efficient way to do this? A data maturity evaluation.
The purpose of maturity evaluations
Clearly, the ability to capture relevant data within a business is an essential starting block for data management, but converting this data into something of value takes the right capabilities, processes, and strategy. This is where maturity evaluations are invaluable, by providing the business and leadership teams with a current state understanding of the company’s Data Management Maturity. This increases the collective awareness of the data assets and ultimately the value of data for the business. The objectives are to improve data management collaboration across different business areas, identify the best opportunities for improvement and to make recommendations as to the most appropriate next steps to becoming a data-centric company delivering value from its data assets.
Whilst maturity evaluations are clearly useful tools for companies who have recently embarked upon their data management journey, as a way to structure their data management framework and baseline capabilities, organisations with active data management strategies should also conduct maturity evaluations regularly as an opportunity to measure progress. This is also a good chance to assess and re-align the data management strategy with any potential changes in business priorities or industry landscape.
It is important to note that whilst the application of a structured and standardized framework of practices is crucial, the level to which an organisation needs to be mature in each area is dependent upon its business strategy, business model and operating model. Data management solutions are therefore not ‘one-size-fits-all’ and a maturity evaluation should recognize this.
Assessing maturity: Our Approach
At DTSQUARED, we have an industry best practice framework which allows your data capabilities to be measured and compared against industry peers. For this framework we utilize DCAM, the Data Management Capability Assessment Model, developed by the Enterprise Data Management (EDM) Council. We are proud Partners with the EDM Council which allows us to utilize and apply their model alongside our expertise, in order to deliver our specialist Data Maturity framework.
We believe the complex and lengthy studies conducted by others in the industry, which generate 100+ page outputs, do not return sufficient value to the business, relative to the time and capital invested.
Our approach engages with key business pillars in enough depth to ensure an accurate assessment is made but has the added advantage of reducing the latency between planning and returning value to the business. The post-evaluation findings summary is succinct and easily digestible, in a language that all areas of the business can relate to and understand, clearly articulating next steps for improvement to support data roadmap development.
The key to accurately assessing a company’s level of maturity and thereby successfully aligning the data strategy to attainable objectives is to ensure six core data management pillars are reviewed. The defining characteristics of each are highlighted in the table below.
|Data Management Strategy||Outlines an organisation’s long-term vision for defining, organising and governing data at an enterprise level, in addition to the business value of data management.|
|Data Management Programme||The catalyst that drives and manages data strategy implementation through alignment of business requirements with operational objectives, across the organisation.|
|Data Governance||Sets the standards, policies, metrics requirements and oversight measures to ensure enterprise wide alignment to data management strategy. Determines roles and responsibilities, including ownership and stewardship of critical data throughout the enterprise.|
|Data Architecture||Determines the means and processes through which metadata is populated and evaluated; defines and implements data models; works with stakeholders to identify and define data domains and oversees data usage to ensure consistency.|
|Technology Architecture||Drives the technical support of the data management strategy by ensuring the systems and platforms through which data is collected, transformed and stored operate at maximum efficiency. Ensures existing infrastructure is updated to conform with data strategy.|
|Data Quality||Defines and determines the overall quality of the data. Uses profiling, data quality rules and monitoring metrics to ensure that data meets identified quality thresholds. Enables root-cause analysis and remediation of data quality issues.|
For each of these pillars DCAM specifies best practice Capabilities and Sub-Capabilities that an organization should have in place and scores them on a scale of 1-6; 1 representing ‘Not Initiated’ and 6 indicating an ‘Enhanced’ level of maturity which is fully embedded in the operational culture, with the goal of ongoing improvement.
The outputs of the Maturity Evaluation can be categorized into four deliverables that will facilitate the transition from investigation to action. These are as follows:
- Objectives, Approach and Summary of questions for each pillar
- Maturity Evaluation score and Summary of Findings for each pillar
- Summary of the best Targets of Opportunity and Recommendations on Steps to Progress Maturity Levels for each pillar
- Suggested Roadmap to Progress Maturity Levels and start to develop a Data Management Framework for the organization
Given that 90% of the world’s data today has been created in the last two years, it’s safe to assume that businesses have a tsunami of data heading towards them in the near future. Akin to any asset, the companies that can most effectively manage and drive meaningful business insights and value from this wave of data will have the competitive advantage and dominate market share.
That is why now, more than ever, companies need maturity evaluations. Having a clear and up to date understanding of data management capabilities combined with a robust roadmap will ensure businesses can continue riding the ever-growing wave of data and the opportunities it brings. Those that miss the boat risk being swamped by organisations who adopted a data-centric approach early on.
Given the number of maturity evaluation providers and the variety of frameworks and approaches offered within the industry, we understand that selecting the right method for you can seem overwhelming. We have helped many of our clients with maturity evaluations, from leading banks to social housing.
You can download a quick guide to our Data Maturity Evaluations here, which demonstrates the process in a little more detail and shows how we help our clients to unlock opportunities through a maturity evaluation.
Data Maturity Evaluations - aiding digital transformation
Send download link to:
If you would like more information on selecting the appropriate evaluation framework and guidance on how it can complement your business objectives, please get in touch and we will set up a complimentary tailored session.