The power of data to enhance strategic decision-making can have a transformational impact on business. In the current digital business landscape, however, organizations in the public sector and across commercial industries are dealing with such a vast amount of data that many struggle to realize its true value.
Many organizations lack access to—or even awareness of—data that is disparately located in silos throughout the organization. The issues of fragmentation and insufficient policies related to trust, data sharing, and data management throughout an organization must be solved to maximize business performance. If these problems are left unaddressed, the ability to make the best possible strategic decisions, across critical lines of business, will be impeded.
While a degree of progress has been made by organizations that have begun to move some data to centrally accessible environments for analytics purposes, this is only scratching the surface. There’s still a large discrepancy between what most organizations could potentially achieve and what current practices and capabilities allow.
This article will help begin to bridge that gap and set a strategy to realize the full value of your data.
Senior leadership is often unaware, or unable to access, all the data assets that exist across an organization, whether that’s due to a flawed approach to data management, a multitude of disparate technology systems, or departments working in silos unwilling to share information.
With so many different systems and platforms at play in businesses today, the number of data sources, and the lack of connectivity between them, can often restrain business performance.
Introducing a more unified approach to data management enables organizations to gain more actionable insights from data analytics. This can’t be done simply by making all data easily accessible in one technology system. Often a cultural shift is necessary to encourage more open data sharing as appropriate within a secure, access-controlled environment and consistent practices with data storage throughout the workforce.
Enabling more strategic, data-driven decision-making requires the cooperation and collaboration of the whole business. Effective, actionable use of data across the organization involves striking the right balance between data sharing and availability, and data protection and privacy.
Solving challenges with fragmentation requires a holistic, organization-wide data strategy, predicated on your specific needs for strategic analytics and data use cases that support your organization’s mission. This type of strategy will succeed or fail based on employees’ acceptance and adoption, so a cultural shift within the organization must be driven by comprehensive change management, education, and leadership. This cultural shift must be supported by consistent best practices for data management, governance, security, sharing, and analysis throughout the company.
Creating a comprehensive data structure designed to support strategic decision-making provides business leaders with intelligent, actionable insights from data in critical areas of the business. This will deliver a range of benefits, including:
The following five steps are key to establishing a successful data strategy that will improve organizational access to timely actionable insights.
1. Stakeholder Buy-In
An early step involves educating your C-suite, and other key stakeholders, to make them aware of the opportunities being missed without more intelligent and strategic use of data. Demonstrating the potential value to be gained from sophisticated analytics and data-driven decision-making will help gain stakeholder buy-in, which is crucial to the success of your strategy. The C-suite must lead by example and drive this shift towards a more data-centric culture from the top down.
Ensure the C-suite is ready to collaborate to open data-sharing pathways where necessary. You should also prioritize change management, to establish the necessary data- and information-sharing mindset, as well as implement the required standards for data governance and security, across all lines of business.
2. Set a Strategy Specific to Your Organization
Any strategy planning should start with the specific challenges and needs your organization is facing, relative to your current level of maturity with data. Improving access and availability of data, to better analyze and generate actionable insights, is critical. But the steps you take to achieve this must be determined by where you currently stand as an organization, as well as the ways in which your overall mission will be augmented by data-driven decision-making.
Set goals to solve problems that will enable better use of data and communicate these clearly to all involved. Appointing a data champion, or otherwise assigning appropriate roles and responsibilities to stakeholders, will help to keep everyone accountable.
3. Conduct an Audit to Build Data Asset Awareness and Intelligence
Developing an up-to-date, acute understanding of your existing technology infrastructure, and all the data sources within it, is an important early step. You cannot begin to use or analyze data if it is not logged accurately and managed consistently, or if it is unavailable to the appropriate stakeholders. This step will require you to investigate and audit all the data assets and sources in your organization and detail them in a thorough data catalog. Breaking down silos fully may not be possible for several years in certain organizations, but proactively seeking out greater intelligence about the data in these silos could unlock new opportunities much sooner.
4. Foster a Data-Centric Culture of Collaboration and Sharing
Championing organization-wide collaboration and information sharing will be pivotal to realizing the full value of your data. This relates to the earlier point about building a strategy not just based on technology and processes, but also the people who will be affected by these initiatives. Your strategy will not succeed if the people within your organization are unwilling to buy into it. This means prioritizing education and change management, including appointing data champions or ambassadors who will be focused on breaking down silos and encouraging adoption of a collaborative approach to data.
Not only do your employees need to embrace a more open culture; they must also actively learn and follow best practices to ensure all data is kept and shared, accurately and securely.
5. Avoid the Allure of Cutting Corners with New Technology
A common pitfall is adopting new technology solutions, such as an all-in-one data management platform or advanced automation tools, without a genuine need for them. Don’t make the mistake of viewing any one technology platform or tool as the silver-bullet solution to your data challenges. If you’re not at a certain level of maturity with your data practices, then introducing artificial intelligence (AI) technology, for example, will prove to be a failed investment.
It’s far more important to initially focus on solving the problem of fragmentation by establishing company wide standards for accurate, consistent data storage, management, and availability. Get an up-to-date understanding of your data assets, robust data governance, and the cooperation of your workforce in place first, rather than pursuing shortcut solutions and quick fixes.
A comprehensive data management strategy will help optimize performance across all areas of the organization. To manage the complexity involved, many Chief Data Officers (CDOs) seek to partner with an experienced specialist to ensure this data strategy is executed successfully. An outside perspective can be particularly useful in helping your organization understand your current state, fill the gaps, and create a clear roadmap to reach your desired outcome.
A vendor-agnostic partner with a diverse analytical and technological skill set, can work with your existing infrastructure while also recommending more effective tactics and cost-efficient tools to optimize the ways your organization deploys data and boost your decision-making capabilities.
This article is co-authored by Blair Campomizzi, CJ Donnelly, Bob Dunmyer, and Leigh Sheldon.
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