There are many reasons why employees might fail to embrace digital initiatives, why they don’t avail themselves of the new data analytics tools that have been provided to them, don’t take advantage of or trust information provided by data analytics systems, or even exhibit overt resistance to the entire venture. They may be set in their ways and reluctant to change, may not fully understand how important digital transformation is to the survival of the company, may not grasp how data is applicable to their specific role, or may fear that digitalization, machine learning and AI will take away their job.
The task for any company seeking true digital transformation is to overcome those obstacles and create a culture where employees instinctively, automatically, even involuntarily look to data first, no matter the problem that needs to be addressed or the process that needs to be improved.
This is data instinct.
In an organization with a culture of data instinct, data informs all decision making. Every assumption, every standard operating procedure, everything that companies do because “that’s the way they always did it” — everything is questioned and challenged by data.
Over time, employees who cultivate a data instinct don’t just participate in digital transformation projects launched by the chief information officer or other executives. They become empowered to use data to figure out how to make themselves more productive; those insights are shared collaboratively with colleagues; and digital transformation takes on an organic, bottom-up momentum of its own.
In an organization that has cultivated data instinct, anything that does not serve the goals of the digital enterprise is stripped away based on what the data is saying, and processes are rebuilt in a way that makes them more effective and efficient. When your business has this kind of culture, the “digital transformation project” no longer exists. It is not seen as something with an end date, something to be achieved. Digital transformation just happens.
Settling for traditional KPIs
In a typical organization, objectives are usually tied to financial performance or other generic measurements — in other words, every company in a specific vertical industry uses the same metrics as all of its competitors do.
Slick and intricate reports are produced for an audience consisting of the chief executive officer (CEO), the board of directors or other C-level executives. By definition, these reports provide a top-line, simplified picture of the company’s business performance. The time and effort required to make the raw data presentable means they are produced monthly or quarterly. They don’t reflect real-time conditions, and if the only customer for the data is the CEO, there is no common buyin across the enterprise.
The operational level view of what’s happening at the company is disconnected from the C-level picture painted in these reports. They are compiled from information provided by decentralized teams dotted around the organization. This methodology inevitably leads to individuals telling their own stories, protecting their turf, shifting blame; which is not unexpected since they are using their own tools and are focused on hitting their own specific targets. And, when the operational leaders are challenged in front of the board of directors, they discover a pie chart on a PowerPoint slide is, quite simply, not enough.
If a company doesn’t develop data instinct, new technologies such as machine learning and AI can end up being applied to siloed data and inefficient processes. In worst case scenarios, this results in companies making the same bad decisions, just faster. The better performers may be able to move the needle slightly with AI and machine learning, but success remains limited.
In addition, the current way that companies measure success can result in departments being unwittingly pitted against each other. For example, a software development team might be measured by how well it achieves zero defects at product launch. Operational support teams might be measured on how quickly they can resolve issues and clear trouble tickets. If the help desk people run into a sticky problem with a new app, they might declare that it’s a software defect and kick the problem back to the development team. So, to demonstrate their own success, they inadvertently cause the other team to fail.
Data instinct can transform traditional KPIs
Applying the data-instinct approach enables companies to shift from focusing on financials to focusing on what really matters — company-specific business outcomes. By cultivating data instinct, companies challenge all of their long-held assumptions, deconstruct their metrics and develop entirely new sets of key performance indicators (KPIs) that are unique to the organization. Every company has its own differentiators and the best way to measure and grow an organization is to come up with tailormade metrics that assist in achieving business goals.
These goals are not based on financial numbers, so they can be holistic and interconnected. In the developer-operations team scenario, instead of focusing on zero defects and clearing trouble tickets, there could be a single goal of delivering quality products. Or, in another example, instead of number of units sold or the dollar amount of a contract, metrics could focus on customer satisfaction throughout the product life cycle. In either case, decisions are made at a holistic level.
This leads to a new hybrid focus on both business performance and customer success. Valued employees will be encouraged to look for issues and work together with customers to provide real solutions. And customers will receive quality products and services that are constantly improving.
This can only be achieved with a culture that has embedded analytics and data transparency at all levels of the organization. The entire culture becomes dependent on the data the organization has at its disposal, and data becomes the focus of all collaboration. It’s no longer one analyst looking at heaps of data, but a whole team investigating data to reach a common consensus on the best way to proceed.
The organization’s new KPIs may be remarkably similar to before, but they carry new authority. Where before they lay static and lifeless on a slide deck, now they’ve gained context and life. New opportunities for the business to outperform its competition are easily identified, since the culture lives with the data as a companion rather than a burden.
How to achieve data instinct
Building a culture of data instinct requires a significant investment in people, process and technology.
People: The hard skills of the people working for the organization will be required to change. To argue that hard skills are more fluid, whereas soft skills will become more foundational, sounds counterintuitive. But in the new digital enterprise, knowing how to use a specific tool or application becomes less important than creating a culture where individuals overcome their fears of uncertainty and their reluctance to change and can become confident in their ability to leverage data to bring about lasting change.
Process: Complex processes are commonplace in most organizations, and this is an issue that needs to be addressed. It is quite common for organizations to talk about “best practices,” but they are not always applied in real-world situations. Many companies take an existing framework and just apply a bit of tweaking, rather than look at it with fresh eyes and apply data to determine whether the process needs to be totally deconstructed and rebuilt. All of this process change needs to be funded. But as an organization builds its culture of data instinct, improvement opportunities are identified and accepted with little fanfare and without much business disruption. Process change becomes less of a one-time cutover and more of a continuous, gradual process.
Technology: The tip of the iceberg is technology. It underpins everything. Choosing the right technology will enable you to drive change. Vanilla toolsets have simple approaches that will not fit every organization. Technology moves at such a fast pace that you need proper ongoing investments just to keep up. If the enterprise is to operate for the long term, technology needs can’t be satisfied with just a purchase order for more licenses. Organizations will need consulting and services to support the people and the process change that’s occurring.
The recommended approach
In a traditional or agenda-driven enterprise, there is too much focus on massaging the data to get it ready for a specific report being requested by a C-level executive. This often requires so much time and investment that by the time the report appears, the business has changed direction, diluting any potential impact of the report.
A digital enterprise at the latter stages of maturity will be focused on business outcomes rather than the bottom line. Keeping the main goals in mind, every member of the organization is working to achieve and improve. There is autonomy to move freely within the organization to bring benefits wherever they are needed. The pace of change increases, and innovation becomes natural.
With this new approach, the response to ongoing events will change. In a traditional or analog enterprise, the lack of autonomy means that reaching decisions is slowed down by the organizational structure. If, however, everyone is responsible for achieving business goals, problems and opportunities are shared, and goals can be reached more quickly. No one is blamed. At the same time, no one tries to run ahead and solve problems on their own. Collaboration makes the entire operation both sustainable and scalable.
The culture undergoes a massive shift away from punishment to encouragement. Employees find their place and naturally function at a higher level because they are in the right environment. Data, shared with the entire enterprise, will be used to reward, rather than reprimand. This, in turn, will fundamentally change the way employees view data. If the data is good, everyone benefits. When data and data quality are poor, opportunities are missed, and goals may be hard to meet.
What next? Look to the data
We are still in the early days of the movement toward data instinct in the average enterprise, but it represents the future. The next generation of business leaders will possess the instinct to look to data before all else. For example, new research from the Charity Commission for England and Wales indicates that 18-to-24-year-olds who are deciding which charities to support during the Christmas season are far more likely than older charitable givers to perform online research first. In fact, more than 50 percent of young people said they check the commission’s online registry to determine where the charity spends its money and whether it has ever been formally investigated. That compares with only 29 percent of older people whose instinct is to check the data before donating.
Organizations at the forefront of this progression to data instinct across the entire company encourage sharing of knowledge. There is no fear of what the data will show because the data represents an opportunity to see where things can improve. And with improvements identified, progress is easy to chart. People are valued within the organization because their opinion is coming from a genuine eagerness to deliver better products and services, and they are rewarded for the efforts.
This is the kind of organization that everyone wants to work for and be a part of.