Last updated: July 28th, 2020
Driving Business Value Through Data
Organizations can no longer afford to overlook the need to create a strategy surrounding data management. The reality is that businesses cannot only look at their BI initiatives from a reporting and dashboard perspective, which according to Dresner’s Wisdom of the Crowds BI study, is where organizations still place most effort and investment. Companies require a direct correlation and understanding between data and business challenges to ensure that both initiatives are aligned and support the change needed to drive business value. One of the challenges faced by many, however, is knowing how to be strategic about enabling the organization to tackle challenges and actually achieve quantifiable business value.
Why Does This Matter
The analytics market is at a crossroads. Many organizations have been leveraging BI for several years, but still struggle with getting the most out of their data. Solutions may be limited by a lack of operational visibility or by siloed data stores. Because of this, companies cannot gain the proper insight to make decisions about their supply chain, or customers. They might not be meeting targets or understand how to set valid ones that are aligned to executive goals. The implications are far-reaching. If solution providers cannot support business needs, there is a chance they will lose customers. For organizations, this means a potential replacement of current investments, additional time and resources, and new financial investments, without promised outcomes.
There are cases when looking at a net new approach is needed, but some organizations attach project failure to a solution provider, when it is actually based on a lack of focus on strong data management and the lack of alignment between project goals and analytics outcomes. This means that the purpose of most analytics and BI projects may be to drive business value, but without strong data management, this can be challenging, if not near impossible. Organizations need to evaluate why they are not getting what they hoped for; or alternatively, justify what needs to happen to align data and analytics more broadly as this is the only way to truly drive business value.
Instead of focusing on specific metrics or data challenges, it becomes important to identify overall goals and make sure that the current business and data challenges are taken into account. The challenges identified in the figure here are common to several organizations and can be drivers for change. If an organization experiences increased customer churn, it can turn to its data to understand causes, but more than that, it can create programs to enhance overall customer experience and look more proactively at how data and analytics are managed to identify potential challenges before they occur, as well as opportunities to enhance customer interactions. If a business wants to enter a new market or create a new product or service, it can leverage data to do so and create a go-to market strategy to support its goals. These are just two examples of how leveraging data can drive change and support quantifiable business value through analytics.
The most important aspect of enabling business value is to ensure that all initiatives are tied to what an organization wants to achieve – its business outcomes.
At the same time, organizations need to ensure the solutions they select help them drive the change they need to succeed. In many cases, this means looking at what exists and making sure that capabilities support the need to drive change. Otherwise, it may require an evaluation to ensure that a new or updated platform is put in place. And there is no substitute for the effort needed to design and deliver strong tools with data management that can support current and ongoing requirements.
Defining and Delivering Quantifiable Business Value
Here is how organizations can work towards gaining tangible benefits from their analytics implementations.
- Do not make technology choices based on industry fads. Many companies choose solutions based on marketing messages or what looks popular, or alternatively what friends who work in other organizations have recommended. A proper evaluation that identifies how solutions meet specific organizational needs is essential as each business has unique needs and challenges that will affect overall solution choice.
- Make sure business challenges are understood and work backwards to identify what types of data are required and how they should be used. Leveraging technical resources only helps with part of this process. It becomes essential to collaborate with line of business workers to ensure that data management initiatives are tied to desired outcomes.
- Look at technology as a set of systems (broad ecosystem) that work together to solve a problem as opposed to individual systems that need to be managed or analyzed. Most organizations will leverage some form of hybrid ecosystem so ensuring support for both cloud and on-premises data and ensuring automated integration are essential. Additionally, as data requirements shift, support for a changing ecosystem needs to be considered, including what data resides where, and how external data sources and data services will be managed.
- The most important aspect of enabling business value is to ensure that all initiatives are tied to what an organization wants to achieve – its business outcomes. It is no longer good enough to state that benefits of BI are the ability to save time or to enable knowledge workers to make more informed decisions. That is only the start. Tangible outcomes need to be tied to direct cost savings, profit increases, entry into new markets, and being able to meet and/or exceed expected targets.
Going through these steps support the ability to gain business value through analytics. Strong data management cannot be discussed enough, because no matter what companies decide, the work needs to be done. And there is no easy way to get things done, except by expending the effort and creating an approach that is agile and innovative. At the same time, there needs to be a direct correlation between inputs and outcomes to achieve true business analytics success.