BY FAISAL HOQUE
An often-misunderstood term that refers to the data sets whose size or type is beyond the ability of traditional relational databases when it comes to capturing, managing, and processing, big data is generated by sensors, devices, video/audio, networks, log files, transactional applications, web, and social media. Much of it is generated in real-time and on a very large scale, making it particularly onerous for organizations to wrap their arms around in today’s information-rich business environment.
In its most recent business intelligence (BI) survey, BARC identifies the benefits of big data as better strategic decisions (69% of organizations say this is the top benefit); improved control of operational processes (54%), a better understanding of customers (52%); and cost reductions (47%). Furthermore, BARC says those organizations that can quantify their gains from analyzing big data reported an average 8% increase in revenues and a 10% reduction in costs.
Where Do You Stand?
So, are you ready to begin harnessing big data, and if so, just how can you best leverage it to your advantage and make those efforts worthwhile?
According to the CMMI Institute, the major challenges for the data management executives are as follows:
- How do I get my arms around the problem?
- How do I educate and inform my multi-leveled audiences?
- How do I define and plan the high priority initiatives?
- How do I achieve commitment to build and sustain a successful data management program?
What would help?
A data management maturity (DMM) framework that allows an organization to:
- Step 1: Ask what specific organizational capabilities must be put in place for you to meet your short-term and long-term business goals and objectives.
- Step 2: Prepare the processes to improve communications and educate the organization about interdependent business functions.
- Step 3: Decide what internal and external capabilities you need to execute on defined business strategies.
- Step 4: Understand and specify the business value to be pursued.
As a result, it creates a common language to help business leaders repeatedly make such decisions as:
- A common terminology
- Shared understanding among stakeholders
- A clear path to increasing capabilities
- Analyzing implications and impacts of potential initiatives
- Setting target allocations for investment categories
- Evaluating the health of organizational assets
- Determining appropriate sequencing of major initiatives
- Managing risk mitigation across the organization
- A real solution for achieving the elusive alignment and agreement between the business and technology
Over the years, a number of DMM models emerged from the likes of CMMI Institute and other academia. From my own cross-industry work and research, I have published several of them in my own books.
“The Data Management Maturity (DMM) model is a process improvement and capability maturity framework for the management of an organization’s data assets and corresponding activities. It contains best practices for establishing, building, sustaining, and optimizing effective data management across the data lifecycle, from creation through delivery, maintenance, and archiving.” — — CMMI Institute
Below is a model that I have curated from various best practices that will help you understand where your organization’s capabilities are in terms of its usage of data and where it could go.
Focused on seven dimensions (leadership, skills, culture, data, tools, uses, and analysis), a DMM model explores different levels of adoption — from “unaware” to “mastering” — and everything in between.
Here’s how each dimension can be defined within the scope of a DMM model:
When leaders are unaware of the usefulness of data, they aren’t interested in it nor do they invest in data and analytics. Those leaders who are learning the ropes realize that data is important, but they’re not entirely convinced, so they invest small amounts. Those who have mastered big data fully understand how to use it and they rely on it to help improve upon what their organizations are already doing. They use past, present, and forward-looking data for both business planning and decision-making.
Example of Level 3 Maturity — Learning: Know data is important, but not entirely convinced. Invest small amounts. Business plan with some defined and measurable targets though data collection/analysis may not align. Might use past and current data for decision making with some simple trends analysis, learning through experience, building adequate skills.
Unaware and “nascent” organizations assign no staff to the task and don’t make any efforts to cultivate internal skills that will help the organization advance its big data efforts. Moving up the scale, organizations that are developing their strategies understand the different skill sets within data and analytics. They have dedicated, skilled analytics roles established, with several people responsible for data in different roles/teams. The most advanced organizations have high levels of staff commitment at senior, specialist, technical, and administrative levels.
Example of Level 3 Maturity — Learning: Dedicated person/team in charge of data as well as other skilled data people in different teams or roles. Adequate data analysis/reporting skills as part of their jobs with some investment in more advanced skills development. Fairly regular use of external support and advice, mostly around specific tools, systems or projects with some skills development.
Organizations that are unaware of the power of data tend to be populated with leaders and employees who aren’t interested in data, which is generally only accessible to a single person or team (usually junior staff). In nascent organizations, data is seen as the responsibility of “someone else,” with the philosophy being that data should be collected (but it is not seen as a whole team activity). In organizations where big data strategies are already being developed, entire organizations are beginning to use and share data. People from different teams/levels regularly discuss what the data says and how to act on it, and specialist staff within specific teams is starting to use data to ask difficult questions.
Example of Level 2 Maturity — Nascent: Data is seen as the responsibility of ‘someone else’. Recognition that data should be collected but it is not seen as whole team activity. Data mostly sought out and used to support and evidence what that organization already believes or knows. Organization’s culture doesn’t encourage data sharing across teams, though this may occur occasionally verbally or via reports. Basic policies for data protection and security may be in place but not monitored or enforced. Little/no staff/volunteer training.
Organizations in the early stages of embracing and using data don’t collect much of it, nor do they check it for validity or accuracy. And while they may collect data manually for a specific purpose, they rarely update that data. Moving up the curve, organizations that are learning typically collect data and then review that data to determine whether it is meaningful, relevant, and useful (even if it may contain errors). At the mastering level, organizations have very high levels of confidence and trust in data quality, and they invest in resources to collect, clean, maintain, and manage that data well.
Example of Level 1 Maturity — Unaware: Limited data (if any) collected. Not checked for validity or accuracy infrequently, if ever, updated. Collected manually for specific purpose. No external data sources used. Nobody is aware or interested in the data assets in the organization.
Nascent organizations are still using basic spreadsheets and paper to record data, and for basic analytical tasks. Their tools are limited and may not be up to date. Organizations in the learning stage are using databases, CRMs, and spreadsheets in an operational (versus analytic) fashion, with most of the data needing to be “extracted” for further analysis. Masters of big data will keep information on widely-accessible data sources and provide users with the tools they need to be able to directly access both internal and external data.
Example of Level 2 Maturity — Nascent: Basic database, spreadsheets and paper used for recording data. Spreadsheets and reports in databases may be used for basic analytical tasks. Tools are limited. May not be up-to-date, don’t meet current needs, and may not be documented or supported.
Organizations that are unaware or nascent generally collect and use data for requisite purposes (e.g., legal, financial, or funder compliance), with most data being based around activities, outputs, and basic financial analysis and forecasts. Moving into the “learning” stage, organizations start to collect a lot of data on clients (and how they engage), and then focus on capturing some outcomes data. They use data for income generation and for the forecasting of sales and donations, thus resulting in more effective fundraising and/or commercial income. At the mastering level, organizations use data extensively and in interlinked, strategic ways for a wide range of purposes.
Example of Maturity Level 3 — Learning: Collect a lot of data on clients and how they engage. Capture some outcomes data. Historical service user/project level analysis to evaluate performance within depts. Use data for income generation and some forecasting of sales and donations leading to more effective fundraising and commercial income. Better able to adapt to changes in external environment. Able to demonstrate work being done for specific user groups in specific projects. Can start leading conversations with funders, partners, clients using data. Use own data as well external sources to evidence need and some outcomes and impact.
At the learning level, whole organization analyses are beginning to be performed on an ad-hoc basis, with reports collated manually using different sources of descriptive data. There may be some routing, automated analysis and reporting taking place, but for the most part data is arduously reworked for presentation in static reports (and for different internal/external audiences). As they develop their big data strategies, organizations become more consistent and regular in their approaches to data reporting and trends analysis. And at the mastering level, organizations are leveraging forecasting and predictive models to plan for the future needs of their stakeholders, customers, and bottom lines.
Example of Maturity Level 2 — Nascent: Analyses starting to explore service users/customers and target audiences. Analyses may include external data e.g. to evidence scale of need/problems. Basic analysis, using counts and spreadsheets. Use of basic charts. Analysis and report creation skills variable.
Daunting for a lot of organizations — and especially for those who fall into the unaware, nascent, and learning categories — data isn’t something you can ignore and hope that it just goes away. Cumulatively, we’re generating roughly 2.5 quintillion bytes of data every day — a number that’s set to grow over the coming years. The organizations that will take the time to gather the data, analyze it, and turn it into actionable insights will gain a competitive advantage over those that don’t.
Copyright © 2018 by Faisal Hoque. All rights reserved.
Faisal Hoque is the founder of SHADOKA and other companies. Shadoka enables aspirations to lead, innovate, and transform. Shadoka’s accelerators and solutions bring together the management frameworks, digital platforms, and thought leadership to enable innovation, transformation, entrepreneurship, growth and social impact.
Author of “Everything Connects — How to Transform and Lead in the Age of Creativity, Innovation and Sustainability” and “Survive to Thrive: 27 Practices of Resilient Entrepreneurs, Innovators, and Leaders”. Follow me on Twitter Faisal Hoque. Use the Everything Connects leadership app and Suvvive to Thrive resiliancy app for free.