Data Culture, Simplified!

It is said that the technology is not deterministic, it can offer advantages as well as pose challenges, it is so true for data and data driven cultures. Though data has become cheaper to produce and thus more readily available, it can be costly to hire the right resources to analyze the wealth of data that exists. 

Emergence of data analytics and insights is omnipresent in the lifecycle of digital transformation, the organizations which harness power of data must have a data culture since few of these organizations have done something so remarkable the everything they do revolves around data, for example GRAB is no longer a ride hailing company they do and are doing much more since their value propositions revolve around data. On the other hand, many organizations still struggling with basics and have lost the play with their key stakeholders now raising questions on the return of the data initiatives promised to them as key differentiators, they failed due to lack of data culture I presume!

What is Data Culture?

“Data culture is the principle established in the process of social practice in both public and private sectors which requires all staffs and decision-makers to focus on the information conveyed by the existing data, and make decisions and changes according to these results instead of leading the development of the company based on experience in the particular field” says Wikipedia

Data Culture refers to a workplace environment that employs a consistent approach to decision-making through emphatic and empirical data proof. Data Culture DNA enables multiple dimensions of data ranging from but not limited to actual transactions, events, trends, management reports ( Read, Sales, Production, Distribution etc), performance indicators such as productivity, efficiency, turnaround time, service level targets and even the metadata of devices, context and communication / correlations thereof including a wholesome look at the entire data supply chain / pipeline..

Data culture encourages curiosity, challenges assumptions and fosters collaboration within stakeholders laying a strong foundation of trust by providing access to data and encouraging transparency. This develops strong governance standards that give employees / team members the confidence to trust their data and associated insights.

Biggest bottleneck in adopting a data culture for the organization is lack of data strategy, most of the organizations does not have a data strategy. As per Gartner by 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs. Traditional companies do use data to run their businesses, but not as a tool to drive their strategy since their business models are not fully based on metrics and interrelated facts and the results / inferences and are not shared across all levels of the organization uniformly to leverage the power of data to drive outcomes serving business objectives.

Goal is to leverage data and analytics enablement across all levels and roles within an organization that ensures core belief in data, consistency of process and collaborative insights driving the decision-making practices as center of gravity for critical competencies. To enable this change, many organizations today have a designated role of CDO, the Chief Data Officer who’s mandate is to develop a data strategy, entail data literacy, establish the data-driven governance structure, lead the data driven transformation and thus positively influence the entire data ecosystem imbibing the data culture into the organization.

Key Benefits

1. Confident Decisions – Creating Data points around the associated context, collecting and analyzing data, makes it easier to reach a confident decision about almost any business challenge, whether launching or discontinuing a product / service, targeted campaigns, opening new territory or new market for expansion etc. Since Data performs multiple roles of serving to benchmarks on what currently exists and extrapolate / better understand the impact of any decision made or in making since data is logical and decisions are no longer based on gut or instinct alone, by removing this subjective elements confidence is instilled and support is garnered from all corners of the organization.
2. Becoming Proactive than Reactive – One of the key advantages for data-driven organizations is having historical, real-time and predictive data to become more responsive and proactive. Given enough attention and consistency in the data driven practices with right datasets, internal and external co-relation thereof and the right types and quantities of data to leverage get proactive in finding out newer business insights and early warnings, by identifying business opportunities before your competition to by detecting and eliminating risks before they become real and  serious.
3. Better Customer Service – With data-driven approach it is possible to know customers feedback and what’s working or what’s not. Awareness and transparency across an organization help drive remediations, efforts to retain customers, get buy-in, loyalty, engagement, encourage teams to ideate on change and new product, service, or business model development enhance the customer experience,
4. Consistency and Continual growth – After a successful run of first few initiatives based on data driven decisions, the enablement helps create new business opportunities, scale up  revenues, predict future opportunities and optimize current operational costs and thus propels business for next value additions for growth with retaining the earned value for Longevity.
5. Realize Cost Savings – one of the practical use case is to use data to save on undue expenses, optimizing the resources to higher utilization and make well informed decisions around spends.

Key Stages of Data Culture...

Fundamentally, any organization must know the “Ask”, collect the right data to “Answer” those requirements and finally “Action” for the outcomes using its collective wisdom to making decisions. Let us simplify this journey into three “A” stages...

1. ASK - Make everyone think about data, making them data-centric while in discussions or before making any decisions. This involves systems and processes to liberate and exact data from various organization applications, events, historical records to help establish the context for discussions and reviews. In the yester year context this was MIS (Read, Management Information Systems) but was grossly limited to transactions, siloed applications and few players having the control of EDP departments. This is the data that even if exists in the archives is not easily available for establishing trends and put to use unless ingested and normalized into a data set / model governing the “ASK”. The fallout of this initiative is first step of Data Democratization across entire organization.
2. ANSWER - Transform the data into meaningful insights while bundling the context as supporting any thought or belief, this creates trust on the decision-making process and across all stakeholder for collective ownership of the decision as in data-informed stage. The enterprise data hubs, analytics engines, algorithms are few of the means to achieve data normalization, transformation and building datasets that empower the meaningful insights to all stakeholders. in the early stages of scaling analytics use cases must be thought through, in detail, the top two or three feasible use cases that can showcase the business value quickly—ideally within the first few quarters to maintain constancy of purpose, harvesting low hanging fruits. Data Democratization is the outcome of this stage, wherein is the life-board of an organization, liberating the data from the clutches of silos must be dealt with. Analytics capabilities if stand isolated from the business, results in an ineffective analytics organizational structure and the data science thus has very little or no impact and that the business keeps doing what it has been doing without significant or visible change.
3. ACTION – Involve everyone to look at the data insights and find out what to do or strategize a specific business outcome or objective, this is the stage known as data-driven approach and the indication of existence of data culture. Buy in from the board, having a clear understanding as to what data can do to their business objectives should be the imperative. Any decision if not supported by data must be questioned, debated and unless substantiated should be a passé. The transformative evidences result in revised forecasts, new products, sales channels, expansion to new geolocations, distribution means and all allied strategies that are funded and executed accordingly.

Where to start and how to measure the success?

Transitioning to a data culture is a challenge that requires dramatic change for traditional organizations, but the first steps toward that end can be simple ones. Identifying and encouraging data ambassadors and mapping organization’s data supply chain will potentially launch the organization toward managing data as a strategic asset. This mind map will help capture the elements that has a bearing on the data culture of the organization and a definitive role to play at each stage of organizational data culture maturity level and the pace of adoption...

While measuring the progress there are few pointers to keep an eye on, such as ..

• Perhaps most importantly, implementing a data driven culture requires buy-in from the leaders and managers as well; without that, little will change.
• Organizations own data, not employees, yet data collection needs to be a primary activity across departments taking help from employees.
• Eliminate the misconception that only highly skilled mathematicians or data scientists are the only ones responsible for business analytics. Spread accountability broadly and train all employees about the role of analytics in their respective jobs.
• Data analysis is useful to find a pattern within, or correlation between, different data points, subsets of internal and external data. From these patterns and correlations that insights and conclusions can be drawn. Just because a decision is based on data doesn’t mean it will always be accurate. While the data might show a particular pattern or suggest a certain outcome, if there are flaws in data collection process, modelling or interpretation then decisions based on such data would lead to errors and can adversely impact the desired outcomes.
• Data-driven function, departments companies place a high value on sharing. Expand tools to help incorporate both structured and unstructured data insights and implement a single master system for analytics across the organization.
• One dashboard that has a holistic view into all areas of the company will make it simple for employees to present information, make decisions and act on data. siloed initiatives, tools and specialized talent are not enough when applied independently.
• Datasets are a resource that can power growth, not something to be hoarded. Shared data should be utilised by as many employees as possible, which in practice means rolling out training wherever it is needed.
• Even with these practices in place, companies will inevitably take missteps along their path. But that is part of the process of getting there.
• Finally, Chief Data Officer — should be at the forefront of that change and shares key steps that data and analytics leaders can take to make their organization a data-driven enterprise.

It is obvious that each organization will have a different pace and aptitude on the data culture maturity curve, the transformation is complex, requiring departments, functions to rethink, replace longstanding processes with new ways of working and capturing the data at the source. Being sensitive to the goal and the pace to achieve it is a rare balance.

Stakeholder buy-in, training and handholding must occur across areas such as data collection, management and integration, analytics / business intelligence, dashboards and visualization. Without such training in relevant tools, techniques and technologies, exploration and inquisitiveness will grind to a halt and will impact organizational change adversely. The size and type of data that matter vary across organizations and even within, but the transformative power of data itself does not.

In Summary, A data culture offers many benefits, such as enhanced customer engagement, higher service levels, real time feedback, improved efficiency and thus higher productivity. On the other hand, it also helps sharpen corporate strategy, transparency, recalibrate unviable goals and drive the organizational transformation. Clear view of data’s role in the company’s overall mission helps all stakeholders reinforce their connection to data culture resulting in greater good of the organization. What Say?

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Jan 2020. Compilation from various publicly available internet sources, authors views are personal.

Suggested Reading / Acknowledgments.

https://www.forbes.com/sites/esri/2020/01/17/how-one-of-americas-biggest-brands-developed-a-data-culture/

https://www.tableau.com/sites/default/files/whitepapers/tableau_dataculture_130219.pdf

https://www.gartner.com/en/conferences/na/data-analytics-us/featured-topics/topic-data-driven-culture

https://www.thedmti.com/wp-content/uploads/2018/09/Why-data-culture-matters.pdf

https://www.researchgate.net/publication/328233575_Becoming_a_data-driven_organisation

https://www.microstrategy.com/us/resources/blog/bi-trends/6-competitive-advantages-of-data-driven-organizati