We have arrived in a golden age - an age of data. Many companies already rely on active data use, data analysis and make decisions based on this data. This development is very positive, because data-driven and thus evidence-based decision-making, offers risk minimization and at the same time great potential for identifying optimization and innovation.
The basic prerequisite for this development is that appropriate data literacy has been built up in the company as a whole. After all, the hoped-for potential is extinguished all too quickly if the workforce is not taken into account and prepared to work with data. In this article, we want to discuss the question of how companies can effectively make data-driven decisions and take action, what obstacles need to be taken into account, and provide concrete recommendations for action to ensure that implementation is successful.
Fact-based innovation instead of flying blind
By now, almost everyone is aware: Data is an important success factor when it comes to business decisions. Some even consider data to be the oil of the 21st century. Data has always been important to most businesses, and resourceful business people have been consulting it when making decisions long before computers became widespread.
But the more data is collected, the harder it seems to become for some decision-makers to align their strategy with it. That's perfectly understandable. The key word is Data Literacy - the technical term for a basic data competence -. admittedly not an easy concept to grasp, but a very essential one.
Far too seldom is data used as a basis for decision-making in everyday life. The reason for this is not the lack of tools or data experts, but rather the mindset. There is a lack of the basic attitude of wanting to think, work and decide in a data-driven way.
Relying on gut instinct, past experience, or time-honored processes is still the norm in most boardrooms. So far, this concept has worked wonderfully for many companies, but gradually more and more corporations are facing a flood of data that they no longer know how to deal with. The sheer volume of data available today, both internally and externally, opens up completely new possibilities. In this context, companies are at a fork in the road: Do they leave everything as it is or do they take advantage of the enormous potential for growth and innovation?
It's obvious: this opportunity must now be seized. For years, companies have been collecting data to improve products and services, increase customer satisfaction, and avoid bad decisions. The work behind it is done by a few, the much-needed and rare data experts.
In the past, the search for and preparation of data and the insights gained from it required special data experts. Without experts who were familiar with the complicated BI tools, nothing worked. But in today's world, where innovations like Big Data, AI and IoT are changing all industries and only those companies that adapt quickly will be successful, data experts are no longer enough. Non-technical employees must also be able to access and interact with data.
This is precisely why skills such as data literacy are now coming to the fore. Up to now, individual employees in the specialist departments have hardly noticed these changing requirements. Unfortunately, because data literacy is actually not only in demand at work, but it will also play an increasingly important role in private life and in our society. It is the key to the next stage of data-driven companies and a democratic data culture.
At the beginning of the year, the federal government published its Data Strategy and thus not only made data literacy a trendy word in Germany, but also directly declared it part of general education. There is no doubt that this poses a challenge for many a company. Even if the value of active data use has been recognized by executives, the workforce is often still unclear about how to deal with data.
The prevailing fear of contact causes the implementation of data and digital strategies of many companies to come to nothing. One important reason for this can be found quickly. Companies are still reluctant to promote the data skills of their own employees. The misconception that data skills are only relevant to subject matter experts is firmly entrenched, turning a stepping stone into a stumbling block. It is therefore all the more important that data literacy is to be found on more and more L&D roadmaps in 2023.
Data Literacy - an indispensable hard skill?
Data literacy is considered one of the most important and fundamental concepts of the decade. For experts, it is an indisputable success factor in a data-driven world, but many may even be reading the very young term for the first time. Data literacy means data literacy when translated directly from English, implying that the skill is on par with learning to read and write.
This may sound drastic, but it gives an important indication of the direction in which we are developing socially and economically. Similar to literacy, data literacy is about being able to read, work with, analyze, and communicate data.
Nowadays, no one can escape the decisions made by companies, insurers or official institutions based on their own personal and sometimes sensitive data. The concept of the transparent citizen is repeatedly criticized, and not without good reason, because data does not always allow clear conclusions to be drawn. This is what makes data literacy so important.
If everyone in our digital society is able to take a critical look at data, this will ensure greater security and transparency. And companies also have a responsibility in this regard. Data protection can only be observed if employees understand data. Although this has been the hobbyhorse of IT and data departments up to now, data literacy must now be carried into other specialist departments as well. What sounds like a lot of effort, however, also holds great benefits, especially for companies.
Many companies are very aware of the added value of data analytics, but even though they are using data more and more, this does not automatically mean that the data literacy of employees has increased by itself. A Accenture study from 2020 was able to prove that out of four employees, only one feels competent enough to actively use data.
Conversely, this means that three quarters of employees feel overwhelmed when they have to work with data. This results in an urgent need for action on the part of managers to ensure that this overtaxing does not have an impact on performance as data usage increases. In a study by the Data Literacy Project (DLP) 60 percent of respondents admitted that they prefer to make decisions at work based on gut feeling rather than taking concrete data into account. One in three even shies away from working with data so much that he/she avoids it completely and has even called in sick because of IT, data and information problems.
Whether your own employees become roadblocks or enablers for data-driven companies and those who want to become one depends on whether they are taken along for the ride. But there is a bright spot: The DLP study also showed that 37 percent of employees surveyed felt that data literacy training would increase their data literacy and productivity. Executives and L&D managers now have a responsibility to integrate data literacy into their training plans and promote data skills to the workforce.
Here's how investing in data expertise pays off
The McKinsey Global Institute was able to demonstrate clear competitive advantages in a study on data-driven companies. Data-driven companies:
- Acquire new customers 23 times faster
- Retain customers 6 times more likely in the long term
- Are 19 times more likely to be profitable
Data is a real gamechanger for companies, provided they know how to use their data for decision-making and embed it in their strategy.
On the way to becoming a data-driven company, decision-makers face a number of challenges. They quickly realize that data and intelligent software alone cannot make a company more successful. What is really needed is a fundamental shift in thinking and the training of employees and managers.
The type of training can depend on factors such as department, career level, prior knowledge, and yet the entire workforce from marketing to IT should have access to data knowledge. The focus here should be on needs-based, scalable training rather than "one-size-fits-all" training. The advantage of training that is adapted to employees' knowledge needs is that they retain what they have learned for longer because it ideally reflects their work reality and they can apply the new skills directly.
To the starting line
The starting point needs to be well thought out, because a functioning data culture does not simply build itself. In the case of a company-wide transformation, it is advisable to start at the executive level or in higher management and from there move on to the specialist departments. To support this transformation, on the one hand there must be a clear vision and companies should discuss what goals they want to achieve; on the other hand, the executive floor and senior management must act as role models.
Data-Driven Management means making business decisions based on available data and communicating them in this way. It is advisable to form a project team. This team should represent and include all stakeholder and departmental perspectives as far as possible, which enables a reliable determination of requirements and the support and power users needed for each stakeholder group.
Companies and customers aren't the only ones who need to benefit from these new capabilities. They can also offer employees added value for their everyday work. A training course in Python can completely automate simple, repetitive and therefore often boring tasks. This is an additional incentive for employees, who now have more time at their disposal. So if you show them what is concretely possible through the use of data and learning programming languages, an initial obligation becomes an intrinsic motivation.
Data experts among themselves
Hardly any profession is currently more in demand on the job market than data analysts and data scientists. Companies are literally courting good candidates, but once they have brought them on board, they are often cut off from other departments. As a result, departments fall far behind in their data expertise, and poor briefings from these departments then slow down the data teams. Providing access to data and strengthening data literacy in all departments would lead to the questions for data analyses becoming more concrete and useful, and thus puts less strain on the resources of the data team, leaving more valuable time for optimizing business strategies.
However, there is a simple solution to this challenge: There are already some pioneers who ensure that their data experts move closer to the core business and that the entire company is trained in data competence. Not every employee needs to be able to program and create machine learning algorithms, but they do need to understand the language of data and be able to derive insights.
If data literacy is promoted throughout the company, then the departments are empowered to perform simple analyses themselves with the help of dashboards and data experts can do this, where their time is most usefully invested. Data scientists, in particular, add tremendous value through their ability to automate processes and address complex issues.
Data analysts should be able to work very closely with business departments and management in order to understand and advise them properly. It is advisable to make the boundaries between data teams and business departments more permeable and to promote company-wide data competence in order to benefit on several levels at once.
Apples or pears?
Knowing what you want to measure and what to do with the results is the most important basic requirement for data-driven decision-making. Quite a few companies collect data for which they have never determined a use. In order to handle data prudently and also not to overload one's own data warehouse, it is therefore advisable to think carefully in advance about which data is really useful for a survey. After all, data is only valuable if the conclusions it allows provide real added value for the company, its customers and, in the best case, society.
Some companies may be in danger of establishing themselves as data-driven just to serve a trend they can take credit for. But collecting and processing data haphazardly will only cost valuable resources if there is no well-considered question behind it, the answer to which will really move the company forward. Here, companies should be aware of how and for what purpose they want to use this valuable resource and make such decisions with caution and wise foresight.
Decisions with a red thread
Transparency and traceability form the basis for data-driven decisions. In practice, this means that others must be able to understand business decisions of the data at hand and that these decisions are communicated internally. In most cases, there is more than one possible way to address challenges. When decisions and approaches are made transparent, stakeholders can contribute their perspectives. This prevents potentially untrue assumptions from going undetected as the basis for decisions.
All too often we rely on our habits to make decisions, especially when other courses of action are unknown or seem risky. For example, it might look like this: A leader would take half an hour before a meeting to review a list of courses of action and their supporting facts. During this process, data-based suggestions for action can be submitted by employees. The executive can evaluate the suggestions based on the supporting data and go into the subsequent meeting informed and able to justify business decisions based on the data.
These behaviors propagate downward when employees find that their submitted proposals for action are actually implemented based on good, evidence-based arguments. This has the further positive side effect that employees feel taken seriously, it strengthens data-based argumentation and the general motivation increases. Of course, you take a risk when you communicate your decisions transparently. The risk of intelligent questions. If you take this risk, the gain is that you gain new insights and thus increase the trust of your employees in the business strategy.
Data Literacy - a Make or Buy Question?
It is very tempting to simply buy in the required skills, subject matter experts, and software, and thus internally check off the issue of data literacy. Of course, this can be a part of the solution that we don't want to neglect, but companies cannot rely on this alone. The shortage of data experts is increasing every year, and the labor market is already not covering the demand.
As the importance and use of data increases, so will the shortage of trained and experienced data experts. This is one of the reasons why companies are moving towards training their own employees to become data analysts, data scientists and complementary, job-relevant skills. In this way, they solve the problems of the labor market internally and can thus further develop employees in-house.
Software solutions are also helpful in giving non-technical departments access to data and letting them perform simple analyses on their own. But an important prerequisite for this is that data literacy in the company as a whole is so advanced that these employees are able to operate the software and convert the results of the analyses into insights, communicate them accordingly and act on them. This is not a matter of course and requires not only prior knowledge, but also an ability to think analytically in preference to one's own gut feeling.
This make-or-buy question therefore cannot be answered unequivocally. Above all, a company that wants to act successfully in a data-driven manner must not leave its workforce out of the equation. After all, at the end of the day it is the specialist department that must not only be able to understand the data analyses, but should also make them the basis of its actions.
Self-service analytics also play an important role in success. They allow all employees to make data their basis for decision-making. What starts with "aha" moments develops into a corporate culture. Regulated access to data, the trust of senior management and the empowerment of the company's own workforce are key. The impact of this change is typically seen in cost savings, faster decision making, streamlined processes, and stronger customer and partner engagement.
If this change is to be sustainable, new skills, processes, and behaviors will be needed across the enterprise, even if a self-service analytics solution is also deployed. Leaders in particular play a critical role in advocating and spreading enterprise-wide data literacy. If a leader truly believes that every employee can provide the next breakthrough by gaining key insights from data, it will support positive change. Leaders should be aware of the following factors and should communicate them to their workforce:
- Data must be classified as its own kind of capital.
- All business units must be trained to use data actively and correctly.
- Employees need to understand and experience the benefits for the company, the customers, but also their own work reality.
- Curiosity and voluntarism should be encouraged through freely available training, informational materials, and mentoring.
How your company becomes data-driven
For companies considering the shift to a data-driven enterprise, the following questions can help determine their own needs and identify hurdles:
- Does a data strategy already exist?
- What challenges does the business face that predictions from data can help with?
- Is leadership committed to putting data at the center of business decision making?
- Is there an understanding of what data is being collected?
- Have employees already received basic knowledge in the form of data literacy training?
- What are the gaps in data literacy at different business levels and in different departments?
- What knowledge standards need to be put in place to ensure that consistent practices can be followed?
For some, the question may now arise as to whether now of all times is really the best time to position oneself as data-driven. However, many companies are currently facing precisely this challenge and are tackling it. Regardless of company size and industry, many are making this change at the same time.
As a training provider, we offer the support these companies need. We have developed practical training courses for companies and employees from all areas and at all career levels together with data experts from various specialist areas, which are applied to real business cases. This enables participants to apply the knowledge they have gained directly to their own work processes.
Whether online courses such as Data Analyst and Data Scientist or training for managers and employees in skills such as "Data Awareness", "Data-Driven Management" or "Data Storytelling", every qualification need is covered. The course content is scalable and supports the demand-oriented development of data skills, which are so crucial for data-driven change in companies.
Do you also think it is important to establish data and analysis as the basis for strategic business decisions? Then find out more about Training offers, Funding opportunities and get more useful tips on the Building data skills.
Sources:
Federal Government: "Data Strategy of the Federal Government" [05.02.2021]
The Data Literacy Project: "The Data Literacy Project. [05.02.2021]
StackFuel: "Data Literacy: How important are data skills for business and society?" [01.04.2021]
McKinsey Global Institute: "Five facts: How customer analytics boosts corporate performance" [31.03.2021]
Tableau: "How to build a data-driven organization" [31.03.2021]
Accenture: "New Research from Accenture and Qlik Shows the Data Skills Gap is Costing Organizations Billions in Lost Productivity" [05.02.2021]
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