What factors should businesses take into account when designing a data-driven transformation strategy? There is no denying that data has enormous value, and almost every organization has launched a high-profile push toward data-driven transformation initiatives, by examining the potential hidden in their data vaults as a means of gaining – and staying – ahead of the competition in their respective industries. for instance, Data-driven organizations are 58% more likely than non-data-driven enterprises to fulfill revenue targets, and 162% more likely to outperform laggards, according to research.
Though it may sound simple, harnessing information to transform your organization into a data-driven organization is not an easy feat by any means.
In a worst-case scenario, even if a company is ready to transition to a data- and model-driven organization, it may be unsure where to begin. This is because most businesses are now grappling with a host of challenges, including the lack of smart technology, the inability to efficiently handle, integrate, and analyze large amounts of data, and an unwillingness to change.
To address the above challenges, this article aims to provide a general overview of the subject matter, as well as five recommendations to get you started on your transformation path. Are you ready?
What exactly is a data-driven transformation?
That data is becoming increasingly important, particularly in terms of business is an understatement. Data, after all, may reveal a lot about a company’s processes and operations. Every day, 5 exabytes of data are produced around the world. The equivalent is 2.5 quintillion bytes (2.5 billion gigabytes).
Humans will generate 463 exabytes of data per day by 2025, according to predictions. To think that in 2009, the world’s entire digital storage capacity was just 487 exabytes and that by tomorrow’s standards, that capacity will have doubled. In 2008, only three of the top ten most valuable companies were actively pursuing a data-driven strategy; today, that figure has climbed to seven out of ten. Data is used by everyone from Apple to Microsoft to Facebook to Amazon to drive their essential decision-making processes. But what does it mean to be a data-driven enterprise? When we talk about “data-driven transformation,” we are simply referring to any program within an organization that incorporates a higher or better use of data, analytics, and typically new technology. An emphasis on automation, continuous improvement and optimization, the ability to foresee internal and external changes, a flexible attitude, and, most importantly, a culture that fully embraces data and its potential are all key features of data-driven organizations. Some of the uses of data may include but are not limited to the following: Data can be used for analysis and modeling, which can then be used to derive business insights or automate operations. In addition, data needs in terms of data type (e.g. time series, cross-sectional, text, etc.) variety (transaction, demographic, socioeconomic data, etc.), amount, granularity, and recency will vary depending on the unique use case for the transformation while cleaning, storing, and transforming data, doing predictive modeling, churning insights, and implementing operational automation are just a few of the most basic and common tasks that digital technology may help with throughout a transformation.
Always keep in mind that the scope of a transformation varies substantially based on the purpose of the transformation, the extent of change, the technologies used, and the business partners involved.
A step-by-step guide to achieving data-driven transformation
With the exponential growth of data, it’s evident that technology innovation will continue to disrupt businesses, sectors, and global markets in traditional ways. In today’s technologically driven global economy, the barriers between IT and other businesses are fast blurring – perhaps even disappearing. While it is definitely necessary to be aware of the value of a data-driven culture in organizations, comprehending it and putting it into practice are two very different things. So, how can a business become data-driven?
The journey begins with the question, “Why?” (Create a vision.)
When embarking on a data-driven transformation, a company must first define its business vision. For some companies, the transformation will be centered on using data to improve operations and boost competitiveness. Others may be in charge of creating new business models. As part of the visioning process, the macro use cases—the most important initiatives that the business desires to pursue—should be selected.
Establish a data-driven culture: All associates, from number crunchers to beginners, make strategic and tactical decisions based on data in a data-driven culture. Despite the fact that 96 of firms report positive business outcomes from data and AI initiatives, just 24% have created fully data-driven cultures. Companies can construct a comprehensive list of transformative initiatives by referencing their vision and list of macro projects. To construct the list, the organization should employ a structured ideation process, and to determine the time frame, it should apply a strict prioritization system. Data availability, legal compliance, and technical or modeling difficulty must all be considered, as well as economic worth, customer benefits, and strategic importance.
Establish a data strategy: If—and only if—you use your data as a strategic asset informing all of your business choices, you already have a wealth of secret knowledge about your customers, clients, and business that can help you transform your company and take it to the next level. According to a study sponsored by Microsoft and conducted by Harvard Company Review Analytics Services, 55% of business leaders cite data silos and data management challenges as impediments. While an Accenture study suggests that, 64% of organizations surveyed have yet to see a return on their digital investments. What does this mean for businesses?
When commencing on data transformation, a corporation should consider addressing the following concerns: Is your current infrastructure capable of supporting our data value map in the future? Is it better to make or buy? Is it a good idea to store our data in the cloud? Is a data lake required? What role should our legacy information technology systems play in data transformation? To complement its product strategy, the organization should establish a data platform (or data lake) and eventually migrate its legacy systems. Also, everything must be aligned with the year’s goals stated by top leaders for the entire organization. However, each sub-data team’s strategy can be unique while still contributing to the resolution of your most pressing business issues. There is no necessity for all of these solutions to use the same set of restrictions.
Make your data more accessible: The next step is to make your data easily accessible across the firm. This is critical since everyone, from the accountant to the CEO, makes business decisions every day. We all know that data-driven judgments are better decisions, so why not provide them with the information they require to make better choices?
But, for the purpose of argument, let us pretend to be cautious for a while. We live in a world regulated by rules and laws. Some businesses, notably those in banking, insurance, and healthcare, are unable to fully democratize their data. In these circumstances, data leakage would be disastrous for privacy. It would expose the company to direct risk and liability. You may also not buy the idea of sharing all of your data with the rest of the company to prevent confidential information from getting into the wrong hands which can cause you to lose your competitive advantage.
so, how can we ensure that data is made accessible? The solution is to figure out how to get relevant data to the right decision-makers so that they can make better decisions. Examine people’s jobs, figure out what decisions they make on a regular basis, and then arm them with the facts they need to make those judgments. Giving the correct facts to the right individuals will help them make the best decisions possible at the right time.
Select a suitable analytics operating model: A corporation should explain how it wants the data analytics function to work before investing in new data analytics capabilities. It can pick which aspects of the analytics function to bring in-house and which to outsource after examining its internal capabilities.
Establish a data governance framework: The environment in which your data resides is the focus of this final step. Your data assets must be kept confidential and safe. This is a top priority, and by now, all large organizations should have solid data governance, security, and privacy policies in place. The recently conducted “2021 State of Data Governance and Empowerment” study shows that the traditional incentives for implementing data governance have largely remained the same, however, improved data security and quality are now the primary drivers of data governance. Organizations must first get a high level of insight into their data flows, from the point of origin through the enterprise’s final destination. This requires visualizing and quantifying the numerous data pathways, as well as comprehending the many data types and technologies with which these data interact. Only then will organizations be able to put in place the policies that would assure governance from the start.
Scalework can help kickstart your data-driven transformation journey!
The data-driven transformation concept has piqued the interest of business executives all around the world, and it is also driving change in the public and social sectors. Even while it will undoubtedly take some time for you to begin contemplating how to maximize the usage and security of data in every action your company makes, it is still possible. Read our ebook and learn much more about how to reach your data summit.
Scalework’s team of experts can help you realize the full potential of people and data within your business, evolve to the next big thing, stay nimble, and stay ahead of the pack!
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