Being a Data Driven Business- The Advantages and How to Apply It
Understanding what characterizes a data-driven business is imperative for any organization that intends to remain relevant in the future. This is simply a reality that has come about due to the influence of the technology realm on the evolution of business.
Simply put, a data-driven business is an organization that utilizes data to inform decision-makers while enhancing processes and decision-making. While it's true that these days, all businesses process and exploit data in one manner or another, the data-driven business is one that uses data to determine business decisions in systematized fashion, rather than relying solely on trends, history, intuition, and more human (and presumably, fallible) considerations.
The use of data by businesses to improve efficiency and drive innovation is obviously nothing new. In the late 1950s through the 1960s, when the computer industry was in its infancy, there was a great deal going on in this area of which the average consumer was unaware, but which held keen interest for power players in corporate America. It should be no surprise that much of the early integration of computer systems in business took place in banking, financial services, and on Wall Street.
The explosion of productivity resources and refinement of digital technology from the 1990s on has led to exponential growth in the real utility provided by digital resources. This has essentially facilitated the rise of data-driven businesses.
As a process, data-driven decision making (DDDM) involves decisions that are backed up by hard data rather than those which are only based on traditional observational methods. It has proven to be of particular advantage when applied in fields such as health care, medicine, manufacturing industries, and transportation.
DDDM Processes and Misconceptions
We all use data. In fact, we all used data even prior to the so-called Digital Revolution. The difference between how organizations used to do things and how they do things in a data-driven paradigm represents a new modality in how data (garnered from various digital sources) is compiled, analyzed, and utilized.
Prior to computers, analytics were still in use; it's just that the data was amassed and analyzed in a different manner. Qualitative and quantitative sources of information were still utilized by decision-makers, but analysts with paper spreadsheets rather than computers crunched all of the numbers. Trends, history, and the intuition of experienced managers filled in the blank spots.
While digital technology is now filling in many of the blank spots, intuition and the experience of savvy managers remain integral parts of the data-driven business. It has become something of a mythand a bit frustrating to some business strategists and analyststhat data-driven organizations have taken the human element out of the decision-making process entirely, or that this is the direction in which businesses ought to be heading.
Data-driven decision-making (DDDM) has gone a long way toward allowing organizations to make more accurate forecasts, clarify objectives and goals, and increase transparency in many other organizational parameters. However, the experts also agree that expertise, experience, and intuition must continue to play a part in the decision-making process, because these are indispensable resources that digital utilities simply do not possess.
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Benefits of Becoming Data-Driven
The benefits of DDDM are manifold, but in general, its success is predicated on several factors. Among those who play the largest part in successful implementation and use are-
1. Better Accountability and Transparency
DDDM's systemization gives rise to processes that can be relied on by both managers and staff across time, thereby improving teamwork, staff engagement, and morale. While a given executive or manager may be competent and trusted, the capricious nature of opinions (which can change on a dime) doesn't lend itself to processes upon which staff can rely. In terms of fostering long-term accountability and transparency, DDDM is simply a superior modality compared to established methods.
In practice, DDDM aids organizations in addressing risks and threats, thereby boosting overall performance. It establishes that certain policies and procedures will be executed within fixed parameters, taking much of the guesswork out of workers' decisions and reducing the need for micromanagement.
2. Business Decisions are Tied to Insights Gleaned from Analytics
With regard to the intuitive processes referenced earlier, data-driven management saves time in that it allows managers to mine data and immediately engage their experience and intuition. Precise analytical objectives within the DDDM process can save even more time and enhance performance.
DDDM also allows managers to adjust parameters, to test different strategies, and determine what is actually the most efficacious route to whatever the organizational objective happens to be. Finally, when decisions are data-driven, the speed of decision making is dramatically increased, since real-time data and past data patterns are always at the ready.
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3. Continuous Improvement
Continuous improvement is another distinct benefit of data-based decision making. Through established metrics and ongoing observation, organizations become able to monitor said metrics, implement incremental changes, and make supplementary changes based on the outcomes. This serves to improve performance and overall efficiency.
Employing DDDM, established metrics ensure that the decisions made are rooted in facts, rather than the knowledge level or skills of staff or managers. It also allows an organization to scale changes and pivot quickly for the rapid implementation of new policies or procedures.
4. Clear, Precise Market Research Efforts
Through data-driven decision making, an organization becomes better able to devise new products, reliable services, and workplace initiatives that improve efficiency. It also aids in the identification of likely trends before they manifest in markets. Investigating historical data allows an organization to know what to expect in the future, and what to change in order to generate better numbers.
Analyzing customer data helps a business gain understanding of how to establish and maintain good relationships with customers and keep them informed in the areas of new products, services, or business development.
5. Consistency Over Time
A benefit of being a data-driven organization is that there is a consistency of processes over time. This approach also helps those within the organization know how decisions are made, which makes them more effective in their roles. Managers can determine the importance and gravity of the data being collected, analyzed, and managed and take appropriate action.
When everyone in the organization is engaged in the DDDM process, they are able to develop relevant skills, and consistency is improved.
6. Cost Savings and Higher Revenues
Finally, data-driven analysis can pay for itself through cost savings and higher revenues. As has been discussed here, the amount of data flowing into organizations has grown dramatically in recent years. Companies in some industries now stream tens of thousands of data points per second, far more than what was even measurable a few years ago.
Acting expeditiously, managers are able to continually improve the process, which drives cost-saving decisions. The added efficiency, driven by DDDM, itself also adds to cost savings and higher revenues.
Examples- Data-Driven Businesses and Strategies
What businesses are currently engaging in data-driven decision-making, and what strategies are they employing toward achieving higher efficiency and productivity?
Acquisition and Retention
One way in which organizations are using DDDM is in customer acquisition and retention. With the use of a data source(s), businesses can observe patterns and trends in customer-related spending, thereby facilitating better customer service and engagement. Responding to customer behavior and needs based on data is integral to fostering customer loyalty.
Anyone who regularly makes online purchases is probably familiar with the proliferation of retailer post-purchase surveys and nurture emails, which are among the data collection methods used by businesses to this end. The more customer data a business collects, the more patterns and trends the business is able to identify.
With sound customer data analytics mechanisms, businesses have the capability to form behavioral insights that it needs in order to more effectively retain customers.
Coca-Cola is a great example of a company utilizing big data analytics for the purpose of driving customer retention. In 2015, Coca-Cola reinforced its data strategy through a new digital-led loyalty program, which proved eminently successful in bolstering customer retention at Coca-Cola.
Improved marketing Insights
Big data analytics can also aid in improving marketing insights. This covers matching customer expectations, changing product lines, and ensuring that marketing campaigns are influential and properly targeted.
As consumers, we've seen over the years how big brands tended to use the shotgun approach to marketing, spending dizzying amounts of money on advertisements that were hard to escape. Big brands did this largely because they could, due to their enormous revenues. In difficult economic times, this practice becomes unsustainable, however, and in the age of big data, it's no longer necessary. In the past, businesses often lost millions on advertising campaigns that didn't bear fruit. Now, the only excuse for this practice is poor research or poor analysis of the data.
Netflix is a company that has been cited by several experts as one that has a tight program for engaging big data analytics in targeted advertising. Netflix has over 100 million subscribers, which means that they're collecting staggering amounts of data on an hourly basis. Search and viewing data on the platform is used to recommend programming that's in line with the customer's past viewing habits, which keeps customers engaged.
Product Development and Innovation
This is actually one of the biggest and most powerful areas in which DDDM is used in business. Companies that sell products are now collecting as much information as is technically possible in order to incorporate insights into new product lines and in the ongoing improvement of existing products.
Product design processes are established by determining what customers want. Using big data analytics, organizations have become far more able to incorporate what customers really want into new products, because they've already asked the relevant questions and received detailed answers.
A stellar example of a company that uses big data to improve innovation and product development is Amazon Fresh/Whole Foods. When Amazon purchased Whole Foods in 2017, it provided Amazon an unparalleled opportunity to leverage big data analytics while moving into an even larger market. Using data analytics, Amazon/Whole Foods is now able to gain insights into how customers buy groceries and how to improve relationships with suppliers.
How to Build a Data-Driven Business Culture
The motivations and imperatives for engaging data-driven decision making, and the degree to which a business adopts DDDM practices depend upon the organization and its needs. Many companies, such as those in technology, sales, and manufacturing, are putting analytics at the heart of every decision.
According to the tech giant, Intel, building a data-driven culture might sound like a big effort, but to a large extent, it's just a formalization of common behaviors A data-driven culture is one that rewards data collectors across the organization. It's led by executives who want to know what the data suggest, who develop a decision-making structure that includes data analysis, and who base plans on that analysis.
This may seem either simplistic or confusing from the perspective of the business owner, executive, or manager seeking to build or improve upon a data-driven culture, but here are five steps that can help provide a roadmap for becoming a data-driven business.
1. Quantify current performance
What will make your organization better at what it does? This question may be approached by the organization's growth at its current stage. While a startup might be focused on metrics that validate various business models, an enterprise company would focus more on metrics like customer lifetime value.
This question might also be examined in terms of industry. A company that provides services might focus more on the quality of current services, whereas a company that develops products would be more inclined to focus on product usage.
2. Identify key areas
Starting with the various avenues from which data is flowing into the organization, leaders in a company might determine how the sources of data can be used, and in a manner that will bring the most benefit to the organization. The most likely areas will be those that are key to achieving the overall business strategy.
3. Target data sets
Having identified which areas of the business will most benefit from analytics and key issues that need to be addressed, the next step is to target the data sets that will answer the questions that have arisen as a result of the process.
Which sources are providing the most valuable information in the context of the business strategy and objectives? Questions like this will help to streamline the existing data. Targeting data according to business objectives will help to keep data storage costs down while ensuring that the most useful insights are being employed.
4. Collect and analyze data
While valuable data will be collected at every level of the organization and will come from both external and internal sources, those who manage the data will need to be identified. More often than not, these will be department heads and to a lesser extent, managers.
Integrated systems may be required to tie in different data sources so that data may be aggregated and analyzed effectively, and the level of skills needed for analysis may vary depending on the type of data being analyzed. Staff in one department may only require a working knowledge of Microsoft Excel in order to perform analyses, while another department may require staff to be versed in a customized analytics application or suite.
The manner in which insights gleaned from data are presented is the final step, the one in which experience, expertise, and intuition come back into play. This will also determine just how much the organization stands to gain from the data.
In dealing with complex sets of data (say, more than MS Excel might be able to handle), there are many business intelligence utilities available that can pull together complex data sets and present them in a way that makes insights clear to decision-makers and staff across the organization.
Yes, there's a bit more to becoming a data-driven business than a summary decision that the organization will now engage data to drive business decisions, but the process need not be painstaking or onerous. Good analytical tools and the right technology architecture can help in establishing the correct parameters for effective implementation.
Finally, aligning the organization's culture to ensure that staff across departments are aware of the value of data and the best way to make the most of it will result in smooth implementation and long term success.
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