Data Driven Organizations — Concept, tips, and a short guide.
We are in the data age, our lives are driven almost entirely by data. Approximately 5 billion people have access to the internet worldwide, in Brazil alone, the number of internet users increased by 6.4% from 2020 to 2021, an increase of almost 10 million people (the pandemic helped in this growth) .
According to Gartner, 40 trillion gigabytes were generated in 2021, the same amount of data generated throughout past history. This immense volume of data available in the world is growing every second, but you need to know what to do with it. It’s no use collecting data and continuing to make decisions based on guesswork — or using numbers to support a decision that was already irrationally made (the so-called vanity metrics).
What is Data Driven?
Faced with this large volume of data, the term Data Driven has been taking great proportions and prominence in organizations. It is an English term, which means to be guided by data (here we will also use the term: Data culture). That is, using data to improve decision making, strengthen analysis and strategies, identify problems and find solutions, whether for a business, a project, or even for small day-to-day choices. By replacing our intuition ( feeling ) with actions based on analysis, we are able to find behavioral trends or even anticipate scenarios and provide more time to deal with their developments.
“Without data you are just a random person with an opinion.” Edward Deming
For example, data can reveal opportunities, problems, bottlenecks, waste, excesses, and is often also able to suggest promising paths (or even great solutions). By using data in our decision-making, we add important benefits to our team and our organization, such as reducing operational and legal risks, increasing performance, granting more assertive credit, among many other items specific to each type of business.
Having access to infinite data is never enough — if you don’t know how to leverage it , according to Janice Hammond of the Harvard Business School: “In this world of big data and basic data literacy — the ability to analyze, interpret, and even question data — It’s an increasingly valuable skill.”
Main challenges…
“The concept is fantastic and fills our eyes with sparkle! But, as you may already know, this is not such a simple task. It is necessary to deconstruct patterns and build a new way of working.” Eduardo and Sibele ( full article )
“The biggest barrier to data success today is corporate culture, not technological backwardness.” Getting Serious About Data and Data Science
“A 2022 NewVantage Partners survey of executives from 94 Fortune 1000 companies found that organizations still face a potentially long road ahead in their efforts to become data-driven. Less than half of respondents said they were competing on data and analytics — 47.4%; only 39.7% reported managing data as a business asset; just over a quarter — 26.5% — report that they have created a data-driven organization; and only 19.3% indicate they have established a data culture.”
One of the first challenges in the process of implementing a data culture is technology , with regard to storing, organizing, cataloging and consuming all this available data with constant periodicity in an automated and reliable way. It is important to invest in technologies that support this entire journey, as well as in training all the people involved in the process .
At the front of the process , we have the people who will consume this data in search of generating value to the business, creating analyzes and obtaining insights from previously created indicators. This is where the biggest challenge of implementing data culture resides.
“What is not perceived does not exist. If people don’t understand and don’t have a clear perception of the whole process, its motivations and its goals, they will have difficulties to engage in the movement” — book: The new code of culture
Each organization has its own organizational culture, technology and analytics maturity levels , budgets, etc. We need to keep in mind that, as it is a change of culture, it is something that needs to be thoroughly worked on and polished. This process of disseminating the use of data is a long-term process that must have full focus (from everyone involved, especially top management) until it becomes something organic for the team, that is, until the data culture is fully aggregated. in organizational culture.
“Data culture aligns technology , people and processes aiming at the effective use of data to support decision making and organizational strategies. Anything not involving these 3 pillars is not quite a data culture.” Alex Souza
“The data culture transcends technology, it must be incorporated by the data team and permeates the other professionals in the organization.” Eduardo and Sibele ( full article )
How to implement a Data Culture?
Excellent question! How? Here it varies a lot from organization to organization, as it is a culture, it is not subjective. Below are some tips, where you could start thinking when considering implementing a data culture:
- First, understand the organizational culture (of the place where you want to implement it) and how the data culture can be aggregated;
- Make the data culture a management practice: it is recommended that it be initiated by the leadership (top), not by imposition, but through engagement and adherence to the culture, that is, a management that appreciates decisions based on data.
“Leaders need to set the tone that data is a tool, an asset they can use to make better decisions and then reinforce that message at all levels of the organization. This is how culture can help transform companies in terms of how they use data.” — Rahul Pathak — AWS (Data and Analytics)
- Understand the moment of each team, each one has processes that make up its operation. A sudden change can generate unnecessary frustrations, insecurities, leading to rejection of the culture change. Start little by little, always respecting existing processes and flows, promoting iterative improvement and respecting your team’s learning curve, this will facilitate the acceptance of new solutions.
- Motivate training and adoption actions: Aim to promote and diversify training actions for your team (understanding the importance of data is crucial, example: the importance of the correct release of data, what is the impact of a wrong release for the analyzes and for the organization strategies, etc) in addition to encouraging the adoption of analytics tools, so that everyone is comfortable to embrace the new culture.
- Instill multidisciplinarity: Multidisciplinary teams tend to find creative and more flexible solutions to everyday problems. Part of the data culture comes from the technical environment and technology, but don’t forget that it is human beings who will use, interpret and analyze the generated data and metrics. A practical example is the construction of a dashboard : there is no point in having dozens of complex indicators if it is unreadable for your target audience . Process, project, design , communication and UX ( User Experience ) profiles are very well applied here!
- Start small, think big: Don’t try to embrace the world and implement crazy solutions all at once. Trying to map and create dozens of indicators at the same time can take some time and end up generating frustration. Choose to work in short sprints that generate some value with each iteration. Tip : Use the MVP (Minimum Viable Product) vision promoted by Eric Ries in his work A Startup Lean .
- Map and prioritize processes and gains: Starting by solving the biggest difficulties of your team and your peers can facilitate the acceptance of a new culture, new tools and new processes. Consider this when prioritizing where you will start implementing changes. Remember, you want them to use the new solutions proposed, nothing better than to start generating value and proving that this new culture has numerous advantages.
- Develop useful indicators and ensure its reliability: During any change process, there is a certain mistrust, I’m afraid… so that it does not fall into disuse due to the lack of trust of its target audience, who will not hesitate to immediately return to the old, already stable processes.
What is Data Culture not?
Below, some situations I’ve heard related to Data Culture and in my view, they’re doing it wrong…
Case 1: Situation exposed by a Sales Coordinator…
“We are implementing a data culture in the company, taking the spreadsheets we use to control our sales, importing them into PowerBI and generating a more dynamic view to show our managers at the closing meeting how they went our sales for the month.”
That’s not having Data Culture! This is just a different view of data from what they showed in presentations (ppt) for example.
Case 2: Situation exposed by a Data Analyst who was supporting a sales manager…
“We noticed that sales in city X this month were much lower than historically… we went to analyze the sales data further and saw that some orders were not entered in the system in the current month, four sellers did not enter the order in the system until the agreed day (process). We went to understand why the sellers did not launch and one informed that he forgot, and another 3 informed that they were waiting for an opinion from the stock team (via email) to see if they would have the product. We went to the stock team, and they informed that they had not received the email informing them and that it had actually been 15 days since they had released the stocks of some products in the system, because the person who made these releases was on vacation and they did not know how did.
Here, when they started to comment, I even thought they had a well-established Data Culture, but due to process failures and exposed people (training, for example), when questioning this, I was informed that they have a well-established data team, but without well-defined processes and no training and adoption of data culture at the organizational level.
Case 3: Situation exposed by a consultant, who was called to start the implementation of a data culture in a sales
company . The technology director, who was the sales director’s cousin, introduced us, told us what the purpose of the consultancy was and even before the technology director finished his speech, the sales director said: “I don’t know if I’m understanding you well, but this what they want to do, does it match what I do today and what would my role be here?”, the technology director did very well explaining that the objective was to implement something that would help him and not replace him” .
In this situation, we can clearly see that the consultancy’s efforts would focus more on the issue of organizational culture, visibly a family business, completely focused on the directors ‘ feelings (especially those who are not aligned with the new market, which was the case of the Sales director).
Conclusion
To conclude, a quote from Suhail Doshi (CEO — Mixpanel):
“Most of the world will make decisions by guessing or using their instincts. They will be lucky or they will be wrong.”
Organizations that adhere to a data-driven culture usually benefit from more efficient, agile results and a greater ability to devise more assertive business strategies. In addition to more assertive forecasts, development or improvement of products and/or services more suited to the needs of its customers, etc.
Examples of successful companies that are based on data, check out their cases:
· Netflix
· Uber
· Ifood
· Itau
· Nubank
· Spotify
· Disney
· Marvel
Follow the series of articles: Culture of Data: an implementation in practice!
References
The new code of Culture — Life or Death in the Exponential Era (book)
The advantages of Data-Driven Decision-Making
Data Culture: an implementation in practice!
What does it mean to be a data driven business?
Getting Serious About Data and Data Science
