Data is the key to a digital transformation. And being data-driven organizations is the future of businesses that are rooted in digital transformation. According to Gartner research by the end of 2024, 75% of businesses will shift from piloting to operationalizing AI, which drives a 5X increase in streaming data and analytics infrastructure. data-driven decisions can back up the real-time insights and help c-suite members in the organization in making more incisive decisions.
Smarter, faster, more responsible AI, data-driven intelligence, speed-to-insights, value realization, etc., are a few benefits of a data-driven organization that delivers the value for improved business. Creating a data-driven organization in the era of AI is seeing upwards of 20% to 30% improvements in EBITDA because of unblocked efficiencies and more granular financial insight according to the report of KPMG.
Transforming into a data-driven business is the utmost priority of most enterprises. However, if an organization is struggling with digital transformation, it means they haven’t thought enough about the data and the potential of analytics. A good data-driven infrastructure keeps the organization buoyed by hopes of better customer satisfaction, streamlining operations, building state-of-art strategies, and more. Out here for many businesses, a strong and data-driven infrastructure remains elusive. And in this article, we described a few steps to build a successful data-oriented business.
Why is it hard to build an intelligence-driven enterprise?
76% of executives from top-performing organizations cited data collection as essential, as compared with only 42% of companies that lack potential performance from peers- The Economist.
Data is being generated and collected at a speed we have never seen before. The mountain of data applications transforming the internal as well as external operations of a business. Simply, data is changing the reality of businesses from past to future. Yet, for some organizations integrating intelligence and automating leadership roles seems to be a failure.
The benefits and full potential of AI-driven business intelligence can only realize if organizations recognize and tackle the pitfalls in their path to becoming a data-driven business.
Challenges to becoming data-driven:
- – Limited access to data
- – Over-reliance on data specialists
- – Low adoption of BI
- – A deepening talent crisis
- – A prevalent cultural bias
Top practices to build a data-driven organization:
Building a data-driven enterprise is not just about building an operation architecture that uses data insights in making business decisions. Data and analytics leaders must lead the development of the competencies and align work to be consistent with their business objectives for the generation of valuable information. And below described are the top 5 practices to build a data-driven organization.
1. Spark opportunity to become data literate
Companies with strong data-driven cultures are led by top managers who are ambitious about how to incorporate data insights and how to make their decision anchored in data. Becoming data literate is a number one priority for most businesses, and for that reason, businesses should be active in spotting the areas where data and automation can reduce the latency in operations and improve business outcomes. It is important for business leaders to understand the data science prominence for their businesses. By dragging data science closer to business or pulling the organization towards data science by empowering the employees with the language of data, businesses can gain complete knowledge on quantitative topics.
2. Collecting and organizing data into a single-store
Collecting and organizing data into a single-store and making it accessible to key people in an organization can fix the most common missteps at uncertain times. Data is the root of a data-driven organization to make strategic decisions that flourish the architectural grounds of businesses. For instance, leading a global data bank helps in a better understanding of standard data layers to meet the objectives on the C-suite agenda. Banking data in a single-store makes it more reliable and enhances competitive advantage by helping internal teams with real-time market dynamics.
3. Invest in the right set of data tools
Companies that fail to embrace and explore alternative ways of leveraging data are fated to be left behind by those that do. It is important to invest and embrace the right technologies that work for your organization structure and help you in transforming into a data-driven organization in the era of AI. with the mountain of applications available in the market today. It is vital to indulge the right set of tools that help your business in adding value by harnessing the power of data, AI, and Machine Learning.
4. Empower and align responsibilities
Most of the organizations, when considered transforming into a data-driven organization, fail to empower and align responsibilities with internal staff, stakeholders, and investors. The transformation should take place within the nature of business work that is related to data and analytics. The shift doesn’t happen overnight, and it requires an individual who focused on making it happen. The chief data officer is a suitable role to maximize the outcomes of data and analytics for any business. It doesn’t mean every company should adopt the CDO title, but every company should hold an internal or external enterprise help who can prioritize and take accountability to lead the business data and analytical strategies.
What to expect from a CDO?
- – Building state-of-art strategies to leverage the potential of data and analytics.
- – Should drive data and analytics governance.
- – Should empower data-literacy among the workforce.
- – Should drive the future of data-driven work culture.
5. Put data into action to fill competency gaps
A modern data and analytics-driven organization enable both centralized and decentralized work while creating a center for addressing critical competencies by ensuring the practices and collaborative insight creation. Harnessing the power of data and leveraging it to fill the competency gaps can restore the function of various departments within an organization to build a single networking system rather than working on various focal points. For instance, analytics, data management, data sciences, information stewardship, and information product management, to name a few are different branches in a business, yet networking these units can result in gaining a competitive advantage.
Being a data-driven organization is a team sport and requires collaboration and effective and ongoing training programs to tackle futuristic challenges in uncertain times. And we hope this article has helped you in understanding the primary steps to transform a business into an intelligence business. If you resonate with our article, please share your insights with us.