By Robin Pauwels – Business Development Manager
We believe every business can be a data business. Data has more than ever become a key business asset, central to the success of many companies. Leveraging your data in an intelligent way, can give you a tremendous competitive advantage. Companies that don’t evolve and embrace this data revolution, will unfortunately be left behind. In this article, we will talk about 10 of the most important aspects you need to consider when becoming a data-driven business in order to start leveraging true value from your data.
We have already talked about the importance of building a solid data strategy. Let’s dive a little deeper into the most important aspects you should consider when using data as a strategic asset in your organization:
- Get management buy-in
- Measure for success
- Set clear data objectives
- Collect the right data
- Find your analytics methodology
- Choose the right data infrastructure and technology stack
- Build a team with the right competencies
- Create a data culture
- Secure your data processes
- Practice good data governance
1. Get management buy-in
Top-level management is at the core of making important decisions to set the course of an organization. They decide on what new investments should be made, what products and customers to focus on and what market trends to follow. Therefore, it is no rocket science that business leaders first must be convinced on how data can help them in making smarter decisions, improve operations or use it for monetization purposes.
As of today, many business leaders are already convinced that data will transform business for the better and are already engaged in Big Data projects to gain an edge over their competition. Nevertheless, there remains some form of skepticism among corporate leaders. This mainly involves leaders who like to keep their trust in their own perceptions and make decisions purely based on business experience and gut feeling.
To convince these types of managers, you will first have to establish a form of trust which proves that your data objectives are aligned with the strategic objectives of your management. Management is primarily concerned about people, money and time. Show them how data and analytics could help them in making better decisions to improve operations that involve these topics.
When you have established a trust relation with your management, try to learn more about the decision-making processes. Ask some business-critical questions to discover their pain points and the most important decisions they must make on a monthly, quarterly or yearly basis. Try to find the sweet spot where data and analytics could make the difference.
Also, don’t overcomplicate analytics. If your leaders are pure businesspeople, avoid talking in technical terms or by showing complicated graphs or code snippets. Put on your business cap and adopt the business language. Emphasize the ROI for any investment made in data and analytics, and process it into a nice graphical visualization or dashboard. Show them how analytics can help in achieving the company’s KPI’s and business goals.
This should help you in getting buy-in from your business leaders to start building your data strategy and data analytics projects.
2. Set clear data objectives
Having clear goals to pursue, always improves your overall way of working, and therefore also has a positive impact on your business results. This is no different when talking about data and the value it could deliver for your organization.
The best way to set good objectives for your organization and data projects, is to use an OKR framework. OKR is a goal-setting methodology that companies use to communicate their desired outcomes throughout the organization, focus on the most important areas that need improvement, and deliver valuable results for the business.
As a company, you should first have a set of company-wide objectives. These are the strategic objectives top-level management decides on to set the direction of the company. When starting data-driven projects, it is important to set data objectives that can help the company in achieving the overall strategic objectives.
When determining your data objectives, ask business-critical questions, of which the answers could help you in achieving the objectives. Look for answers that can help you in making smarter decisions, improve company operations or to use data as a monetizing asset.
3. Measure for success
When your data Objectives are clearly defined, you need a set of Key Results. Key Results are measurable milestones you set to monitor your progress towards the objective. This is a powerful tool to draw down where you want to go with data and analytics, and what steps you need to take in order to get there.
Next to OKR’s it is also important to define your KPI’s. You need to set numeric expectations of performance and set clear targets to define whether your data projects are successful or not.
For example, if you are using data analytics to learn more about your customers, things like website visitors, churn ratio, conversion ratio and NPS are probably metrics you would like to track.
OKR’s and KPIs are both needed to grow, improve or sustain your data projects. While KPI’s are business metrics that reflect performance, OKR is a goal-setting method that helps you improve that performance and drive change. So KPI’s let you know what you need to analyze to determine the basis for your OKR’s.
4. Collect the right data
Having identified what questions you are looking to answer with data, you can now start to think about sourcing and collecting the right data. There are many ways to source and collect data, including accessing or purchasing external data, using internal data and putting in place new collection methods.
If you want to have a competitive edge, it is always a good idea to put systems in place to collect or generate data automatically, whether it is data generated by users of a product or machine data from the manufacturing line.
With so much data available today, the most important part of this step is to focus on finding the exact, specific pieces of data that will best benefit your organization. So from a data strategy point of view, you need to describe the ideal data sets that would help you achieve your strategic objectives.
There are different types of data you can consider. Generally speaking, internal structured data is the easiest to find and analyze, and usually the least expensive to require. At the other end of the scale, external and unstructured data is often more costly to acquire and more difficult (and therefore more expensive) to work with.
You will probably find that you need more than one data set to find answers for your business questions. It is often better to work with more than one data set to get a fuller picture. The combination of internal and external data often provides the most valuable insights.
To meet your strategic goals, you may well need some structured internal data (like sales data), plus some structured external data (eg demographic data), alongside some unstructured internal data (such as customer feedback) and unstructured external data (eg social media analysis). The ideal strategic approach to sourcing data is to find the best combination of data to get the most useful insights for your business.
5. Find your analytics methodology
Having identified the ideal data for your business, your next step is to identify how to turn that data into useful insights. Analytics is the process of collecting, processing and analyzing data to generate insights that help you improve the way you do business.
In most cases, it involves software-based analysis using sophisticated algorithms. By analyzing data with algorithms and analytic tools, you can extract the insights you need to answer your key business questions, improve operational performance, monetize data, and meet your strategic goals.
Analytics allows you to learn new things, understand more about the world in which we operate, and make improvements across the organization. Therefore, as part of any solid data strategy, you will need to plan how you will apply analytics to your data. This will in turn affect the data infrastructure, technology and competencies that need to put in place.
What analytics you apply will depend on your strategic objectives. And, just as with collecting data, it is important to understand what’s possible with analytics before you can decide the best option for your business.
Try not to get caught up in all the exciting opportunities that analytics brings. Organizations are doing very cool things with analytics, but what works for one business may not work for yours. The challenge therefore lies in finding the best, most accessible, most achievable analytics approach for you.
Advances in analytics, AI and Machine Learning are moving so fast that it is safe to assume that new and improved ways of extracting ways to extract value from data will emerge very soon. It is therefore worthwhile putting together a wish list of how you might like to analyze data in the future. It is not unreasonable to assume that some or all of the approaches on your wish list will become a reality in the near future.
6. Choose the right data infrastructure and technology stack
Now you know how you want to use data, what kind of data you need, and how you might want to analyze that data. The next step is to create a scalable data infrastructure and architecture with the right technologies. This means in deciding on the software and/or hardware needed to collect, process and store data, and turn it into insights.
A Big Data infrastructure might go beyond the typical data infrastructures and technologies most companies are counting on today. These existing tools are perhaps in the form of SQL programming, relational databases and data warehouses. These work quite fine, but the developments around Big Data technology urges companies to rethink their data infrastructure.
Thankfully, technology advances like Cloud computing and distributed storage have opened up new data possibilities for businesses, allowing them to tap into the power of data without making heavy investments in on-site data storage.
There are a number of elements to consider when building your data infrastructure, like collecting, storing, cleaning, accessing, processing and analyzing data. For each of these ‘layers’, it is important to explore the key considerations and commonly used tools.
Your infrastructure requirements will depend heavily on how you are looking to use data, what data you want to work with, and how you need to question that data. Therefore, each company’s setup is in most cases unique. It really depends on your business case.
7. Build a team with the right competencies
At this point, you have decided on the right tools and technologies for turning data into insights to help you achieve your data and strategic objectives. Unfortunately, your data infrastructure is not going to build itself. Therefore, another key ingredient of your data strategy is developing the right data skills and competencies.
In this phase, it is important to not only look at your technical skillsets. The ability to relate data to the business’s needs, or to communicate key insights from data to people with no technical background is as important as the technical skills you need to build an infrastructure or analytical algorithms.
There are two main paths you can follow to build data competencies in your organization. One is boosting your in-house talent through hiring data scientists, engineers, or architects. If there are few availabilities on the job market, try to invest in training your existing people.
Another way to build competencies, is by outsourcing the data analysis. There are a lot of data engineering and data science companies on the market, focusing solely on providing consultancy and building tailor-made data and analytics platforms.
Building these competencies will highly depend on your strategic goals and limitations such as time and budget. It is also possible to combine the two approaches. For example, you could seek to train some of your people in analytics, but also need to work with an external partner while your own people build their knowledge. You may build and nurture data skills in-house which perfectly suits your everyday decision making and operations, but you may need some external analytics manpower for a one-off data project further down the line.
We believe the most essential skills you need to find are the following:
- Business & communication skills
- Analytical skills
- Computer engineering skills
- Statistical and mathematical skills
- Visualization skills
Finding unicorns who consist of all these skills is very hard. The trick is to build teams with the right blend of skills that works for your organization. This could lead to partnering someone with relevant analytical skills with someone who is great at communicating insights to the business
8. Create a data culture
As we talked about it in our first step, next to getting buy-in from management to start using data as a strategic asset, it is also very important to get buy-in from the whole organization. Therefore, you need to build a data culture where data is recognized as a key business asset, and it is used at every level of the business to improve operations and maximize business outcomes.
The shift to a data culture often gets resisted by a ‘this is how we’ve always done it, and it worked fine’ mentality. This is why top-level management should be the driving factor of a data culture shift and needs to cascade it down through every layer of the organization. Those at the top must lead by example and use data as the basis of what they do.
It is also very important to use the insights that data gives you. Managers need to act upon the insights found to encourage others in the organization to do the same. Use those precious insights, demonstrate positive outcomes, and it will be much easier to get buy-in from others.
A data culture is about everyone in the organization understanding the value of data and how it can help the business succeed. Communication is therefore key. Leaders and managers should spend time engaging people in the data strategy, stressing how it will benefit the organization and its employees and customers.
Be very open with your employees about what you’re measuring and why, especially when it comes to employee data. You don’t want your employees to become nervous by giving them the feeling that they are being watched in a ‘Big Brother’ way.
If certain teams or individuals are resistant, use their pain points to show how data can improve their working environment or make their job easier. People are far more likely to be comfortable with data if you’re honest about what data you’re gathering and the positive impact this will have.
9. Secure your data processes
When you consider data as an asset, you need to take its security very seriously. Therefore, your data strategy should also take account of data security considerations, like the to prevent data loss and breaches.
For example, any company dealing with personal data is responsible for its protection. Thus, wherever possible, it is a good idea to use anonymized data that doesn’t identify an individual’s details. When this isn’t possible, you should take measures to ensure the data is secured.
There are many solutions a business can put in place to secure data and prevent data breaches. Such measures can include encrypting your data, having systems in place to detect and stop real-time breaches, and training your staff so they never give away secure information.
Data breaches can have a huge impact on your business, in terms of legal costs and financial compensations, as well as the damage done to a company’s reputation.
It is therefore very important to focus on securing your data and processing when executing your data strategy.
10. Practice good data governance
Data governance refers to the overall management and caretaking of data, covering usability and integrity, and security. You should be aware of the moral and legal requirements and regulations concerning every step of your data operations, and have firm policies and procedures in place to govern every step. Practicing good data governance is about making sure you’re not breaking any laws, that you have the correct permissions and metadata in place.
It is important to have policies in place to determine exactly who has access to data, and who is responsible for maintaining the quality and accuracy of that data. A good data governance plan should also define who is the owner of various data within the organization and who is accountable for various aspects of the data. Clear procedures need to be set out for how the data can be used, especially if your company is dealing with personal data.
Naturally, your data governance program should ensure the company is compliant with regulations, and set out procedures for maintaining this compliance, such as regular audits. It is very important to make sure you are compliant with every piece of legislation that affects you. If not, it is possible that your business’s biggest assets will turn into your biggest liabilities.
There are a lot of important aspects to think through when adopting a data-driven mindset. Always start from your own business challenges and objectives, and find a way to achieve them by using data. Build clear frameworks to measure your success and look for the right data and technology stacks to set up data pipelines and intelligent algorithms for turning the data into valuable insights.
When taking your first steps in becoming data-driven, try to find a valuable partner that can guide you through the process. Use their expertise to build your own in-house competencies for future developments.
Make sure you’ve got everyone on board, from management to your employees on every business level, for building a data culture and adopting a data-driven mindset. And lastly, make sure you have the right policies and procedures in place to act according to the law and to make sure you secure your data to prevent data breaches.