Progressive organizations have acknowledged data as an asset when it comes to business strategies, and gained a competitive advantage by leveraging their enterprise data. Data Analytics enables users to make tactical decisions based on data-backed business intelligence about the enterprise, its competition, the customers, and the ecosystem they are operating in.
A large number of organizations are still considering investing in analytics, and are practicing it based on current requirements. These organizations need to have a clear understanding, that they need a lot more than investments for analytics tools and personnel, to successfully incorporate analytics into the organizational structure.
The foundation is laid by a data-driven culture, the key ingredient for analytics success.
Essentially it is an organizational culture that is connected to data. A work culture where business decisions at every level of an enterprise are made based on data-based evidence. Replacing drivers such as a theoretical understanding of the industry, and gut feeling; with data, augmented analytics, and business intelligence.
A data-driven culture is not established by simply accruing vast data sets but through Data Democratization. Where data is available to decision-makers and stakeholders at every level of the organization, and they analyze it to derive contextual insights to base their decisions on. When an organization makes this a practice across business functions, with the aim of gaining a competitive edge, we can recognize them as having a data-driven culture.
To successfully integrate analytics, start with a resolution to be a data-driven organization. Recognize big data and advanced analytics as business functions, evaluating every project through analytics and assessing how to best leverage the data.
In corporate structures, the top brass should advocate for big data analytics, influencing every business function to glean actionable intelligence from enterprise data. Each business unit should also have trained analysts on the team, to streamline insights generation from valuable data, and aid in data-backed decision-making.
Even though data collection and analysis are fundamental to building a data culture, it is crucial to be able to present the insights in a comprehendible language. And advanced visualizations help to deliver the business intelligence to the end-user.
Creating a data-driven culture is thus dependent on evaluating every business function and facet through data, and making tactical decisions based on both insights and business experience.
Data and data management form the building blocks for fostering a data culture. Robust data maturity and high-quality data speak volumes about an organization. It attests to the fact that there are checks and balances in place to sustain the data quality. Because the insights will only be as good as the data being analyzed.
There is a need for metadata management aligned with specific KPIs, and Data Lineage capabilities to track data from its source, and help understand how the data evolved. An effective data governance framework is key to maintaining healthy data maturity. Focusing on the storage, quality and consistency of data, along with the creation of sound data and analytical models.
Data must be stored in a manner that is easy to access and aligns with a business strategy that can create value. Frameworks, processes, and incentives must be in place to support analytical decision-making. The process of developing data and analytical capabilities and using the analytic insights in strategic decision-making is not a simple task, and very few companies have been successful in their attempts.
Leaders have to set up the culture that they want to establish within their organization. Leading by example, they have to build a structure where analytics is integrated into every business unit, and data-based insights are a strategic asset for every business decision.
To develop the analytics capabilities with efficiency, a leader needs access to critical data, and to hire personnel capable of handling the data. There is also the need for investing in cutting-edge analytics tools and setting up business processes to support that data-backed decision-making.
A good framework for evaluating the analytical processes is to review if the processes in hand can effectively function to build and deploy analytical/predictive models and at the same time measure the business impact of these models.
To successfully deploy analytics across business functions, organizations need not just analytics tools but data scientists, and qualified personnel for data mining, analysis, and visualization. And even after deployment, analytics success depends on whether the company’s resources are centralized or decentralized.
Decentralization facilitates simpler collaboration between business functions. Centralization on the other hand enables data experts to operate within a single business unit, making it easier to access enterprise data and gain a comprehensive understanding of business initiatives.
Analyzing data is as much an ‘art’ as it is a ‘science’, and organizations need to give equal importance to their personnel as it does to analytics tools. Once can expect unsatisfactory results if the organization is technology-centric, and holds analytics software in greater regard than its analytics professionals.
To bring the focus back to the analytics personnel and meet the quality of cutting-edge analytics tools being made available, there is an absolute necessity for data literacy. It implies the ability to read, understand, and interpret data, to draw meaningful conclusions and insights.
A data-driven culture requires wider access to enterprise data to a lot of personnel who may not necessarily be equally qualified to analyze data. But depending on the business function, everyone should have the requisite level of data literacy to be able to catalyze their decisions positively and draw business insights from raw data. Whether it is through data literacy programs to train personnel, or augmented analytics tools offering high-end natural language querying capabilities, the decision-maker needs to be able to comprehend the data and the factors affecting it.
Data governance models need to primarily ensure that analytics teams are operating smoothly, and are adopting analytics processes for generating business intelligence. Data is easily available and analytics models operate seamlessly once they are embedded within business units leveraging data analytics.
Organizations need to have a governance committee in place, to make long-term business decisions, maintain transparency, and hold analytics teams responsible for the insights being generated. The governance committee can in turn have an oversight/steering committee with the top brass and experienced IT personnel as guiding beacons.
Last but not the least, a data-driven culture quite literally embodies the importance of decision-making and how data should be only the contributing factor behind any decision. Handling data, asking questions about it, and reaching decisions based on the insights gained, has to become an integral process. A culture where data is not reserved for top brass but is a common commodity available from a single source to all employees.
A culture that fosters collaboration and interaction inside and between business units. Where there is the ease of access to, and sharing of data-backed intelligence, through augmented analytics platforms. Where everybody can contribute based on their experience and expertise, and build up the organization with a team effort. Where there is the drive to ideate innovative solutions, born through collaborative efforts towards solving business problems.
Some of the key benefits of becoming a data-driven business are analytics maturity, a decision culture, quick wins, and the advantage of releasing minimum viable products. These are some major advantages that advocate the adoption of a data culture.
There is a significant development in in-house analytics capabilities and in the ability of untrained personnel to understand data and establish a culture of taking decisions solely based on the insights gathered through analytics. The analytics solutions are also a quick win for the organization as they are not only affordable and easy to deploy, but they are fast at delivering valuable business intelligence which drives business impact.
Having such a culture is also beneficial when releasing new products or services into the market. A minimum viable product (MVP) is a primary offering with just enough features to attract early adopters. It is the feedback from these customers that help organizations to develop their offering further and enable it to resonate better with future customers.
A data-driven organization thrives when data is the objective truth and is made transparent through leadership engagement and management empowerment.
The organization grows when data is not locked in silos, and access to business intelligence drives innovation. The organization embraces the future when self-service analytics becomes the norm, and data becomes the differentiator in an uber-competitive market environment.
A data-driven culture is a key to consistency, communication, collaboration, and co-innovation. It unifies the organization from within and strengthens it from the roots.
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