Perspective on BI: Change Your Data Mindset for Informed Decision Making
Instilling a data-driven culture with infused analytics fuels efficiency and drives innovation.
- By Scott Castle
- July 23, 2021
The days of accumulating data for the sake of accumulating it are long gone. Good riddance. What good is a stockpile if you don't use it? Companies the world over have come to realize that data is a precious asset -- when handled purposefully to fulfill goals and objectives large and small.
However, the key to unlocking its true value comes with its refinement and application in everyday situations. Dirty and disparate data certainly add difficulty but, with some technological adjustments, can be overcome. Competitive advantage requires the use of data, not just by the data science team but by everyone in the entire organization and perhaps beyond. This approach can deliver transformation on a grand scale, one that begins with a cultural shift that embraces the data-led mindset.
A large majority of respondents in a recent Harvard Business Review (HBR) Analytic Services survey indicated that analyzed data is essential to their company's innovation strategy. Although data analytics has had a positive impact on customer experience and operational efficiency efforts, survey respondents believe data insights are not being sufficiently leveraged to make improvements or discover fresh business opportunities.
Far too many businesses are not leading with data and analytics. Instead, they have the same data processes and policies in place, netting mediocre results at best. It's time to do things differently and implement data for competitive advantage. It's no easy task, but the results can be incredibly rewarding. By enabling access to data -- across teams, departments, business units, leaders, and every place in between -- your organization can instill a data-centric culture that permeates your business for better decisions at every level.
Breaking Down Barriers
In the HBR survey, respondents mentioned a lack of employee skills and training, along with less than ideal data quality, as significant obstacles to broad use of analyzed data. Training a diverse employee workforce to use specialized BI technology-- essentially asking them to play data scientist, statistician, and business strategist -- to become data-driven is unnecessary and unachievable. Still, organizations attempt this fruitless exercise.
Our phones, smartwatches, and other tech gadgets have spoiled us with on-the-spot information, in context, to help us make our next move. We now expect that same level of instantaneous insight in a work setting. The effective application of data can inform us of developing situations and deliver insights where decisions are made.
Infusing automated data intelligence into processes, workflows, and applications can provide a frictionless user experience with no complex tools or technical instruction required. Employees don't even need to leave their workflows. With analytics infusion, actionable intelligence is presented when and where needed.
However, there is perceived risk in sharing data broadly. In a data-centric organization leaders need to blend governance with implementing useful data throughout the workforce. They understand that with careful planning, they can offset the risks to achieve the business successes that are inherent in a data-centric organization.
Technology itself is an enabler but can also hamper deployment if team members can't access the data when they need it. The constant influx of data adds to the conundrum. Even though it is the job of the data scientist to make sense of the data, this individual doesn't necessarily have an understanding of broader business objectives. It is better to form a consensus to determine priorities in how data might be implemented, basing technology selection on what will fuel better data practices. The company's data strategy is dependent on how well everyone buys into and leverages it.
The employee viewpoint is critical here because the technology has an impact not just on their own team or department but throughout the company. Instead of requiring employees to deviate from their workflow rhythm and comfort level for analyzed data, why not embed analytics in their commonly used applications? By making information accessible at point of need, organizations enable real-time decisions from within a familiar application. This advances the adoption of analytics by anyone who can benefit from insight in the course of their daily duties. It forms the groundwork to improved efficiency, better outcomes, and healthier alignment across disciplines, departments, and the broader organization -- supply chain and partners included.
C-level Leads the Effort
Saying goodbye to data gatekeepers takes executive-level buy-in, but democratizing the data is just one obstacle. A deficiency in skills and training is not only limited to the workforce at large; it can also be an impediment to executives. Other than the chief data officer, leadership tends to think the application of data and analytics is reserved for specialists. It's time to think differently and gain a rudimentary understanding of what can be gained from data to better lead the troops. Setting the example -- by gaining a familiarity of the methods and approaches involved in creating insights from data -- enables leaders to embrace data-informed strategies and nurture a data-driven culture.
An understanding of the insights required, an appreciation for the value of clean data, and the capacity to recognize gaps in the data ecosystem -- these are the factors that allow leaders to re-orchestrate the way operational decisions are made, internally and even externally. This belief in data's unique ability to fuel business value forms the basis for creating a data analytics culture that is scalable.
It is also important to design a strategy that infuses analytics incrementally throughout company processes and operations. For instance, determine a situation in which analytics can garner quick, quantifiable business results. Leverage the data to forecast customer activity, saving time and increasing revenue. Remember, metrics are adaptable. Bring stakeholders to consensus, setting goals to gauge success. Revenue isn't always the end game; perhaps it's about improving customer service, or discovering a need for a new product, or changes to an existing one.
A Culture that Blends People and Processes
Data analytics can only deliver results if people are allowed to use it. When everyone within an organization has access integrated via their existing workflows, they have data analytics at their fingertips to perform their jobs better. Moving from where the data is to where the work is completed is a thing of the past. Instead, the infusion of analytics reveals insights and promotes decision making there and then.
To derive full benefit from accumulated data, organizations must promote and inspire a data-driven culture, starting with C-level commitment and granting each employee data access. However, executives first have to understand data and what it can accomplish. With an enlightened mindset about data literacy and its impact on decision making throughout the organization, leaders can confidently infuse insights into the workday of each employee, knowing that more efficient processes and new business opportunities are likely outcomes.
Technology is certainly part of the formula but it is the data-driven culture -- people supported by intelligent processes that infuse analytics throughout the organization -- that makes it all work.
Scott Castle is an analytics-infusion pioneer, bringing over 25 years of software development and product management experience to his role as VP and GM for products at Sisense. Scott is passionate about turning data teams into superheroes who find unexpected insights in big data and disrupt traditional BI. Previously, Scott held technology positions at companies including Adobe, Electric Cloud, and FileNet. Scott holds computer science degrees from the University of Massachusetts Amherst and UC Irvine. Connect with Scott on LinkedIn and Twitter.