Data Management: Setting the Foundation for Digital Transformation

Discover why data management is important for any digital transformation. Learn data management best practices to kickstart your journey.

Table of Content

    Today, data is more than just an operational necessity – it’s a strategic asset that can be leveraged to carve out a competitive advantage.

    That’s why savvy organizations are prioritizing DX initiatives that make their data work harder for them.

    But – there’s a catch. In order to successfully unlock the value hidden among their massive data sets, organizations must already have a strong data management foundation in place before moving forward (spoiler: most don’t).

    In this blog post, we’ll explain why data is the foundation for any digital transformation strategy – whether it’s an expensive, culture-altering initiative involving the entire org or something more mundane like improving budgeting, forecasting, or production processes.

    In today’s rapidly changing business landscape, digital transformation has become a critical initiative for organizations of all sizes and industries.

    What is Data Management?

    Data management is the process of collecting, storing, maintaining, and using data so it can be used to generate business value.

    Effective data management is essential for core business functions. For example, professional services organizations need accurate time-tracking data to manage projects, generate accurate forecasts, and make profitable decisions.

    How Data Management Fuels Digital Transformation

    In this digital age, you can’t do anything without data. Well, technically, you can, but it’s not a great idea. Because data is the lifeblood of any business, data management is critical to your success – and survival.

    By analyzing your data on a regular basis, you can make small tweaks that will have a big impact on business outcomes.

    You can avoid the pain of poor decision-making – using data to prevent waste, improve experiences, and pursue the right opportunities.

    You can see what’s working and what’s not. And, why you’re getting the results that you’re getting by digging into the data. You can ID root causes and weak points in core processes, then use insights to explore and test possible solutions.

    Research from MIT/Databricks revealed that data management is key to scaling AI. The challenge, according to researchers, is that becoming AI-driven starts with a data architecture capable of handling big data workflows.

    So, think – data streaming, machine learning, data engineering, and so on – as well as a unified platform that can provide flexibility, insights, data governance, etc.

    In any I, if you don’t have a solid data foundation, you can’t transform.

    What Happens When Data Management Goes Wrong?

    Sadly, many orgs struggle to turn data into high-impact wins.

    Per Forrester, most organizations are currently dealing with process-related issues. 37% say they have trouble adapting to customer needs. 30% struggle to gain insights from their data, and another 30% struggles to accurately measure their success.

    These challenges point back to a data management issue. They’re working with inaccurate, incomplete, or totally disorganized data – and because of this, they’re not getting the results they’re looking for.

    Implementing a Data Management Process

    Like other “DX essentials” like the cloud, AI, and your ERP, mastering data management is not a transformation. It’s something you have to take care of first – otherwise, everything you implement/build/etc. will be informed by bad data – and generate a lot more of it.

    To unlock the value in your data — and avoid running into serious problems, you’ll want to make sure you really nail the building blocks of data management.

    Here’s a quick look at what that entails:

    Unify Your Data Ecosystem

    Your data management strategy should first focus on providing a unified view of all critical resources and entities that might have previously been stored in various silos.

    According to McKinsey, a data ecosystem should center on two key priorities:

    • Creating a collaborative environment. You’re connecting different user groups so they can work together toward shared goals. It’s about ensuring everyone is working from a single source of truth. But, more than that, it’s making it easy for everyone – IT, marketing, finance, etc. – to work with data.
    • Generating measurable value for customers and the organization. You’ll also want to focus on building value with processed data. So, leveraging insights to improve process efficiency, pursue new business models, and monetize data through new channels.

    Done right, a unified data ecosystem should enable businesses to make informed decisions using accurate, trusted information. It should provide managed and secure access to all business data on a self-serve basis. It should also support process optimization and product and service improvements.

    Choose Your Data Steward

    Here, you’re trying to figure out what data you want to collect, as well as where that data is coming from. For example:

    • Behavioral data might come from your website, social media platforms, and other channels. So, you’ll need to consider how you might unify data from third-party sources with your website analytics and CRM data.
    • Financial data might live in your ERP system, but that data also informs everything from sales and operations to project management. If you’re using a collection of fragmented apps to support core business functions, you’ll need to figure out how to bring that data together in one place. And later, how to protect sensitive data per regulatory requirements.

    On top of that, it’s a good idea to take inventory of all data sources, as some of the data you capture will be coming from unexpected – or underused sources.

    Select Storage Solutions

    Data storage solutions depend on the type of data you’re collecting and how you plan on using it.

    For example, if your goal is to use data governance as a force for transformation, you might centralize all data in a single Enterprise Data Lake (EDL).

    By bringing all data together in one lake, you can eliminate duplicate data, connect silos, and set rules that enforce compliance and security standards across the entire organization.

    You might even embed governance rules into the app development process to ensure that all new solutions align with your data policy.

    Or, you might opt for a “project management approach” that organizes data into different domains. Here, data is part of a curated collection of insights, pulled from a number of data sources. In this case, you can manage data by business area, project, or use case.

    Map Data Flows

    Mapping your data flows can help you surface opportunities to improve existing processes or data management practices.

    To do this, you’ll need to ID the following elements:

    • Data sources
    • Internal databases
    • Customers
    • Employees
    • Relevant data sets
    • Processes

    Then, look at how data flows between users, departments, and data sources.

    Here, your goals are identifying storage, access, migration, and security issues, as well as how each department/team currently uses data.

    Define Data Management Objectives & KPIs

    Next, you’ll want to define some goals for your data strategy. You might use insights gleaned from the data mapping process to inform your high-level objectives – or not.

    Either way, here are a few things to consider as you start building out your plan:

    • What do you hope to achieve?
    • What problems need to be solved?
    • Do you need access to real-time data?
    • What is the value of your data?
    • Which data sets require extra protection? Think – customer information, financial records, trade secrets, etc.
    • How will you integrate data from various sources?
    • How “data literate” are your employees?
    • What skills need to be improved?

    Naturally, goals vary widely between organizations. For some companies, it’s about connecting silos and bridging information gaps. For others, it might be improving research efficiency, enabling faster, smarter decisions or building standardized, replicable processes.

    Whatever your goal, you’ll want to select the right KPIs for measuring and tracking your results.

    For example, if you’re trying to improve data quality, you might focus on measuring data consistency, accuracy, and reliability.

    When Microsoft transformed its internal data catalog, it measured the impact of connecting people to high-quality, discoverable data, it tracked things like engineering efficiency and the ability to answer specific questions.

    Implement Data Governance Practices & Quality Controls

    According to Microsoft, a modern governance strategy shouldn’t get in the way of progress – it should enable innovation and agility. Internally, Microsoft developed a data strategy that leverages AI to automatically respond to data issues as they emerge.

    The strategy initially focused on connecting silos and centralizing data, then gradually, used insights from process and usage data to roll out automated, scalable controls to enforce proper use.

    Microsoft Enterprise Data Strategy
    Source: https://www.microsoft.com/insidetrack/blog/driving-effective-data-governance-for-improved-quality-and-analytics/

    The EDL data lake includes built-in governance capabilities that can be applied to all enterprise analytics. Azure DevOps can be set up so that data governance rules are baked into the code of any new builds.

    You might also use Microsoft Purview to manage and govern data across multiple environments. It comes with a visual data map that makes it easy to integrate all data catalogs, apply labels to sensitive data, and enable access permissions.

    Additionally, you’ll want to look for solutions that continuously validate both the actual data and the data models you use in various apps and productivity tools.

    Many modern data integration platforms incorporate automated data validation into workflows, so you’re not creating extra work for anyone. You can create rules that enforce formatting standards, retention practices, consistency, more.

    Data validation is a key part of any data management strategy. Without it, you run the risk of making decisions based on a false reality. And, that, of course, means negative outcomes for your customers, your reputation, and your ability to compete.

    Lock Down the Entire Digital Estate

    You’ll also need to ensure that security protections are embedded throughout your entire data ecosystem.

    Security needs are determined by several factors, including regulatory and privacy requirements, storage solutions and workflows, and what types of data you’re trying to protect.

    Here are a few key security protections you might use to keep your data safe:

    • Encryption. Encryption prevents unauthorized use of critical data. So, even in the event that it’s intercepted by bad actors, that information can’t be leaked or tampered with.
    • Identity & access controls. These solutions prevent credential abuse, phishing attacks, and straight-up data theft by allowing you to define access permissions and continuously verify user identities.
    • Monitoring & alerts. Monitoring systems and processes provide deep visibility into all data repositories. An SIEM like Azure Sentinel can be used to monitor systems for malicious traffic and automatically surface alerts when it perceives a threat.
    • Physical security. Finally, you should also leverage hardening tactics to protect data stored on physical assets such as on-prem servers, devices, and industrial equipment.

    Use Automation to Preserve Data Integrity

    According to Deloitte, manual processes and data management practices without high-quality data and governance is a blocker to transformation.

    Analysts advise business leaders to put together a data governance strategy that spans the entire organization – calling it a mandatory first step in any DX program. They also should prioritize investments in data transformation tools that leverage automation.

    Beyond saving users a ton of time, automation serves a more protective function. BMC experts say that while automation isn’t a regulatory requirement, it does make it easier to accommodate new regulations, maintain compliance, and avoid excessive maintenance costs.

    For example, rules-based automations can prevent bad data hygiene by reinforcing governance, regulatory compliance, and brand guidelines. That, in turn, means users have fewer opportunities to make small errors that create big problems down the line.

    Other data transformation tools aggregate, clean, classify, and enrich raw data – and extract insights that can then be used to support critical business cases.

    Final Thoughts

    With businesses relying more heavily on digital technologies to drive growth and transformation, data management has become an urgent priority. Regardless of what transformation goals you’re pursuing or why, reaching those critical milestones hinges on having access to accurate, real-time insights.

    So, before embarking on any transformation journey, you need to make sure you’re starting from a solid data management foundation.

    Velosio is a full-service Microsoft partner with the experience, knowledge, and resources needed to transform data management practices – and everything else.

    Contact us today to learn more about our solutions, services, and how we approach digital transformation.