Every company in the digital economy is striving to become data driven to boost customer experiences, gain market intelligence, and for strategy formulation. Without data based decisions, companies are likely to experience losses, declining market share and internal problems including management constraints. This leads to the question: Why is data important?

Data according to Ken Green, an investment banker at JP Morgan “drives innovation, insights and risk management, which makes companies smarter and competitive”. The sad reality is that most companies make costly mistakes in pursuing a data driven culture¹ such as excluding training and communication on data and culture systems. Without these values, achieving a data driven culture becomes challenging.

To start, engaging the C-Suite² to adopt the data driven culture will make the data culture within the organization go into effect. While many companies try to make more effective use of data, #analytics and AI, the lack of a data culture undermines data/analytics capability. At the same time, incompetent decision making by the C-Suite executives can flow from the lack of a culture.

Role of CEO’s in Data Driven Culture

The CEO’s have a hard time implementing a data driven culture and lack of awareness, but that does not mean an organization cannot make progress to a data driven culture. Just as CEOs are counselled on their communications and leadership skills³; they can also be moved in the data domain through coaching.

From there, other C-Suite level executives can learn from the CEO to bring this data driven culture together. Then, these practices propagate downwards as employees who want to be taken seriously have to communicate with senior leaders on their terms and in their languages.

The Situation Analysis

Understanding situation analysis can help any organization turn into a data driven culture. Evidence used in a situation analysis shows that a data driven culture can lead to better, faster decision making, greater customer insights, fewer mistakes and ultimately a competitive advantage. Employees and organizations are always in situations every day when making major decisions, especially in risk management.

Because of the importance of supporting risk taking, the ability to rapidly establish situational scenarios is critical. Situation analysis enables program planners to articulate the nature and extent of the problem, identify causes and contributing factors, and consider the direct and indirect consequences of the problem. The analysis goes in depth and looks at what the original problem was and how this came about or how long the problem has been around for.

Then, the factors are considered by those understanding and look at causes to determine where they effect the problem. Finally, the consequences of the problem are identified. All throughout this, #data is derived and gathered by risk management to promote the right decision making skills and guarantee fewer mistakes will arise during the culture change.

Proof of Concept

Asuccessful data driven culture must have a concrete proof of concept, which is a popular way for businesses to evaluate the viability of a system, product, or service to ensure it meets specific needs or sets of predefined requirements. In analytics and data, promising ideas greatly outnumber practical ones. A better approach is to engineer proofs of concept where a core part of the concept is its viability in production.

When it comes to the evaluation of data science solutions, proof of concept should prove not just that a solution solves one specific problem, but that a system will provide widespread value to the company. This will bring a data driven culture to a range of an organization’s strategic objectives.

Enabling a company to have a concrete proof of concept can have positive outcomes within a data driven culture. However, for these outcomes to occur, data scientist within an organization have to determine the right data, understand where the data came from and will the data be all together.

The Analytical Approach in Data Systems

For many organizations, adopting to the analytical approach in data systems/decisions can take some time, but in the end, are always effectual. Adopting a data-driven culture is a must for organizations looking to stand out from the competition as well as attracting and retaining employees. By generating insights from various data sources within an organization, learning and development programs and professionals evolve to benefit employees and the organization.

With this, a data driven culture can only flourish with the right technology in place, adopting to this analytical approach in the latest data systems/decisions. Data driven culture leaders should consider the benefits of next-gen technologies such as AI. #Artificialintelligence and #machinelearning are being used to help companies better understand employee learning behavior.

Overall, technology in a data driven culture can adopt an analytical approach, which is the use of analysis to break a problem down into the elements necessary to solve it. This will help with decision making as well as #datasystems since organizations look for the right information and data to make these decisions. Adopting this analytical approach with all other components will make a data driven culture effective in any organization.

Engaging Teams for Data Driven Culture

The biggest mistake an organization can make is sidelining their data scientists during this rapid data driven culture change. The pursuit of data driven decision making can make business leaders in an organization dream about data science. They believe that artificial intelligence can instantly transform their business.

However, it is critical for these organizations to be on the same page with #datascientists. This will result in workable use of data to drive key decisions within an organization. It is crucial for all parties to be completely aligned, envisioning the perfect triangle that the whole organization can follow and support.

At the same time, the organization and data scientist need to build on their past successes and achievements. For this data driven culture to work in any organization, the data scientist are meant to do the communicating, where they can deliver the right information to the organization without having it transferred down through the ranks.

Meaning no frontline marketing person in an agency or business unit leader should collect the data. With better alignment and a more productive mindset between business leaders and data scientists, there will be more opportunities to use data to improve decision making and achieve better outcomes.

Make Data Culture part of your Organization

Building a data driven culture within an organization is uneasy to do but it can be done. Understanding that data can help with overall decision making is something organizations look for. With the right #data and data scientist as well, a data driven culture can help an organization understand their employees and what they are accustomed to in the workplace.

It is hard to always educate employees and the organization on the information of a data driven culture. However, when an organization and its employees understand a data driven culture, it is crucial towards their cause. Across industries, businesses are shifting to data driven structures, using key business insights to produce faster and more customized products and services.

A data driven culture can help an organization in a tremendous way, leading them into the right path of their future with all the information gained. Many organizations are changing to this culture tactic to gain advantage over their competition and it is working. Organizations will be led down this data driven culture path sooner than later.

Works Cited

¹Data Driven Culture, ²C-Suite, ³Leadership Skills, ⁴Situation Analysis, ⁵Proof of Concept, ⁶Learning and Development, ⁷Next-Gen Technologies, ⁸Analytical Approach