Data analysis should have a purpose, be set on the right foundation and always be conducted with adaptation in mind. We have neither the time nor money for mistakes. Sounds simple, but how does it work in practice?
On the one hand, you hear that over the last few years, more than 90% of the digital data ever created in the world has been generated, while only a few percent of it has been analyzed. Even less of it has been used for business purposes, although those who have done so boasted of improved marketing efficiency, increased sales or reduced costs in industry magazines. Without a doubt, data is being talked about more and more over the last few years – and not only in the context of legal regulations related to the entry into force of GDPR regulations.
More and more often we collect or reach for data and conduct analyses. These are usually one-off projects, even experiments, carried out by single experts or analytical teams detached from the whole business. In many companies, however, initiatives related to data still seem to more like a research project than the basis of a modern business model.
Are you wondering why this is happening? My conversations show that many marketers believe that data-based strategies are difficult to implement. Moreover, they are even more difficult than other marketing solutions they have had the opportunity to implement in recent years. At the same time, practice shows that, feeling the pressure, whether from superiors or the market, CMOs often move straight to tactics. Meanwhile, without a clear understanding of where they are going, it is too easy to get lost along on the way – to run processes for months, make decisions about high investments in infrastructure, hire cost-generating teams. And finally, why is there no ROI despite so many efforts?
Determine the target point, but also the time when you need to reach it
If you don’t know where you’re going, you’ll get somewhere – but the chance that you’ll get where you want to be is relatively small. You will analyze all the data in every possible combination, and you may not see the values at the end of the process. Additionally, excess data will hinder your efforts to find valuable insights. You will hire people who, although they will be experts in data analysis, but not knowing the specifics of the industry, business model, will not bring value to the organization, because the conducted analyses will not translate into the company’s operations.
The list of potential stumbles is much longer and expensive for business. That is why it is important to set clear goals at the very beginning of the process, without taking the context in which your organization is located into account. In other words, the question is what do you and your teams need data for? What does your organization need data for? What information do you need here and now and which, for example, due to the specifics of the industry in which you operate, you can wait for?
As we know, starting with a vision of the end plays an important role in achieving every action. The same goes for data. The more specific goals you set yourself, the easier it will be to develop a plan, what kind of data you need to achieve the desired effect and to divide the plan into stages.
Check where you are on the map of your journey?
Like every change, the journey into the world of data and analysis takes place in stages. Before you make any move, it is essential to know where you are in a given moment. In other words, when you know what goals you want to pursue, you need to assess what stage you are at. This is obvious when you want to visit a museum during a tour of a foreign city, but this rule is not applied as often in your daily work.
This is just as important when you have the challenge of starting to lay the foundations for data collection as well as in a situation in which your organization seems to be advanced in the use of data. Paradoxically, in the latter situation, finding yourself on the map is more challenging, but it is no less important to determine how big the gap between the current and desired state is. On the other hand, the analysis of the current state often shows that the fastest way to start is to use what is at your fingertips.
Analyze the available means of transport, check the grid of connections
Is the data you already have sufficient to achieve your goals? And if not, then what will you have to collect or buy? How long will this take you? What do you need for this? Don’t go too far, there are more possibilities than you think. Remember that your goals should be the main the engine of a clear strategy and that data alone is not enough!
For data to be able to change how brands develop their business from scratch, how they build positive experience with consumers along the way, 4 components are crucial:
DATA – from all relevant business sources
PLATFORMS – key technological components for creating, capturing and consolidating data in all sources;
PROCESSES – defined roles and responsibilities, clear business priorities;
PEOPLE – resources to develop, support, launch and implement data-driven processes.
After analyzing the data sources that are currently available in the organization and those that are acquired, the collection and analysis will significantly affect the achievement of business objectives, not underestimate how important it is to have answers to additional questions:
- What tools will you use to collect this data? Where will you store it? Which data will be linked to each other and which have no common denominator?
- Think about what kind of people you need on your team? Include people on your team and use the possibilities of different people throughout your organization – working with data is a team sport. Analyze what resources are worth reaching out to companies that specialize in data and what resources are worth having in your organization?
- How should the process of teamwork change so that it doesn’t remain with the old habits of data bypassing? The question is about the work process not only of your team, but also of other teams in your organization. In particular, it is worth analyzing how the cooperation will look at the intersection of two worlds: sales and marketing – departments that usually use data most often and at the same time do it in silos.
Start with the nearest area, but with a vision of a journey around the world
In other words, start with small steps, but have great ambition. Jumping into a deep pool before you learn to swim can be a serious mistake. Start with the data you already have and make some small decisions to help you achieve your short-term goals. Such quick successes help you break through the silos and build enthusiasm with your team, but also the necessary commitment and readiness for change in the next steps, which a wider process will certainly require.
From experience I can say that – just like meditation – using data at some point should become more of a habit than a learned skill. For this to happen, it has to be of real value, so don’t collect data just to collect data – make sure you actually use it, that you know what kind of processes support it.
To sum up, the best indicator of success of data-driven processes will not be the quality of the available data itself, or even the level of employees’ skills, but the translation of data into information that supports specific business decisions. Therefore, to be able to transform analytics from a “research project” into the core of a business model, the change must start with you. Without leadership from above, commitment from the company’s management, it is more difficult to identify key business questions, support collaboration between teams, adjust processes.
It is crucial to find the right process for you. You could say that all data is important, but not all data will be important to you or your company! It’s also important that when you feel you don’t quite know what kind of information you need to collect and how to use it, contact specialists. Most probably you have neither the time nor money for mistakes.
Would you like to learn more? Write to me: firstname.lastname@example.org