Building a data-driven organization is a popular goal among the business community.
As confirmed by a global survey run by IDC on over 1,000 respondents, many CEOs and organizations across the world claim to be in the process of becoming more data-driven. But is the data-driven frenzy just a temporary trend, or is there something solid behind it? Should data really drive everything within an organization?
In this article, we will explore the importance of a data-driven approach and the main elements that characterize such practice. In addition, we will look at the challenges that organizations face when it comes to utilizing data and the role of technology in facilitating and accelerating this process.
When talking about data-driven approaches, the same insights and results emerge from pretty much every report on the topic: the majority of executives want their organizations to become more data-driven, and are investing or aim at investing in data management solutions, tools, and data-focused personnel.
“We are a data-driven company”, “we are transforming to a data-driven company” or “we define our company as data-driven” are just some of the popular quotes you can read on the web or hear in conversations nowadays.
The reality though is that most organizations are still very far from having a proper data culture implemented in their processes and operations. Just by looking at the outcomes 2021 IDC survey we mentioned above, it is clear that while executives may say that a data-driven approach is a top priority, only a small percentage of them have taken practical steps toward using data to their advantage. And even fewer organizations are actually succeeding at it.
But is it really that critical for a business to be data-driven? And most importantly, how do you determine whether an organization has a truly data-driven approach?
The growing role of data inside organizations
As data availability increases, organizations are focusing more and more on trying to take advantage of it in many directions. Some common objectives are better serving customers, identifying new business opportunities, growing sales and improving processes, creating first-mover advantages, etc.
And using data undoubtedly brings its benefits. According to a Forrester report, data-driven companies “are growing at an average of more than 30% annually”. In a report published by Boston Consulting Group, the majority of the top 10 innovative companies in the world are data firms. Based on the evidence provided by McKinsey companies that are using data-driven B2B sales-growth engines report above-market growth and EBITDA increases in the range of 15 to 25 percent.
However, too much (low quality) data can actually hurt organizations and become a cost center that doesn’t bring any real value. Today businesses run the risk of collecting too much information, storing it inefficiently, and taking bad decisions because of it.
That is why being able to manage and analyze data in the right way is now a must.
But is that enough for calling your organization data-driven? In the next paragraphs, we will explore several definitions of data-drivenness and will outline the main elements that contribute to it.
What does it mean to be data-driven?
There are many definitions and explanations of what “data-driven” means. The most simple definitions include concepts such as the incorporation of data or using numbers and facts to drive an individual or organization’s decision-making process. Others refer to the gathering and analysis of data to make informed decisions.
What do all of these definitions have in common? They focus completely on a single step (the decision-making step) that in reality comes only at the end of a much more complex and lengthy process.
We are talking about the retrieval of data, its storage, treatment, enrichment, augmentation, standardization, and end of its analysis. The goal? Transform a raw piece of data into reliable, consistent, and insightful information, from which you can extract value.
Therefore, a data-driven approach is related to the whole data management process. Digital4biz defines a data-driven company as one that considers data management a strategic pillar of its business. Although this definition is much more comprehensive as it includes the management of data rather than just its final use, it is still missing several steps.
The role of people and culture
Knowing how to handle data is not enough. Getting to a data-driven organization requires building a data-driven culture.
Carl Anderson in “Creating a Data-Driven Organization” adds some practical indications and additional concepts about being data-driven. According to him, it all resolves to build tools, abilities, and, most crucially, a culture that acts on data. Therefore, on one side you need to have the right tools to guide you, and on the other, the people in your organization need to have the skills and promote a culture that acts on data.
Building a data culture becomes then a necessary element for a data-driven organization. “Data becomes woven into the operations, mindset, and identity of an organization. A Data Culture equips everyone in your organization with the insights they need to be truly data-driven, tackling your most complex business challenges, comments Tableau.
What does this mean in practice? Integrating data into daily practices and operations, so that all individuals inside the organization, from top-level executives and team leaders to front-line workers, include it in their activities to support decisions.
Beyond data culture
While it may be simple in theory, the concept of data-driven implies a significant shift in the mindset of people inside the organization, who need to be willing to work on their data abilities and become data literate.
Data literacy, which can be defined as the ability to read, and understand data, gain insights from it and communicate them efficiently, is the main challenge when it comes to creating a proper data culture, as confirmed by studies from HBR and Gartner.
So culture alone is not enough and needs to be complemented by the right abilities to properly perform all the actions that are required to efficiently manage and assess data.
And abilities need to be paired with powerful technological solutions that allow us to put in place the right behaviors and scale them beyond individuals in a fast and sustainable fashion.
The right data, accessed and used in the right way
Even if all teams are able to work with data, they won’t be able to use it to their advantage unless it is easily accessible, reliable, and of high quality.
So in an organization where employees struggle to find even the most basic data, how can they make decisions with it?
Unfortunately, bad data is the norm. While businesses keep collecting new data points, very few empower employees to access them easily. Usually, you spend more time trying to find the right data, organizing it, and structuring it, than spending it on actually analyzing and taking decisions from it.
How do we use data, the right way? The process for a so-called data-driven organization goes through several steps: data collection, data access, analysis, and reporting, and all they need to be supported by a proper technological solution.
Everything starts from the retrieval of relevant data. In many cases, a (relatively) small amount of high-quality and reliable data can be better than billions of useless info points that, at best, will require hours and hours to be prepped before use.
Data has to be accessible, which does not simply mean “available”, but rather that can be used straight away. Whether it is a relational database, a NoSQL, or a Hadoop database (just to mention a few options), it is essential that data is provided in a form that fits with the use case, so that any analyst can focus on analysis, trends, and presentation rather than on data preparation.
Data cannot be siloed within specific teams or functions or shared only with analysts and highly technical teams. In order to properly build and spread a data culture within the organization, information needs to be extended to a wider number of parties that have to be put in the position to understand and use them. When more data is available to more parties of a system, the whole value that they provide has the potential to become greater than the sum of the parts.
Reporting, Alerting, and Analysis
Data in itself has little to no value. The value lies in what is done with it.
Most organizations, after data, is collected and analyzed, create static excel reports that show what has happened (e.g. how much was collected last month) or underline specific events and alerts (e.g. defaults are getting close to the contractual trigger). However, reports and alerts are basically backward-looking views describing that something has happened in the past.
Can a business be satisfied by simply being notified that something occurred?
Of course not. In order to gain value from data, you want to know what caused an event, what generated a certain trend, and which elements contributed to a specific KPI.
Data-drivenness is all about causal factors: only by understanding the reason behind something, organizations or managers can craft plans or sets of recommendations.
Why are collections slowing down? Which borrowers are not matching their payment obligations?
In fact, having an explanation is just the start of the process. We need to know what actionable insight we can derive from the data.
What should be done to revitalize collections? How can late players be supported to be able to get back on track? Or how can I prevent further exposure to a category that is showing deteriorating performances?
Data-driven organization – Where do we start?
Becoming data-driven, and by doing so, achieving a sustainable competitive advantage from data and analytics is very challenging. Most D&A projects are not very successful. According to Gartner, only 20% of the Data and Analytics solutions deliver business outcomes. Notwithstanding that the report is a bit aged, 1 out of 5 is a very disappointing outcome.
So where should an organization start? Technology, culture, abilities, or maybe something else? As we have seen above all three elements are important and should be activated as soon as an organization embraces data-drivenness as an objective. Technology and abilities can support the first part of the process described in the previous sections: enable analysts to run analyses and write up their findings.
However, it is the culture that sets up the mindset and process to take notice of those findings, trust them, and act upon them. Data culture is also the hardest step to achieve since it is a collective status that has to permeate a whole organization and cannot be confined to single teams or projects.
Our take at Cardo AI about the data-driven approach
Based on both literature and our own experience, we believe that data culture can be strongly supported by thoughtful technological solutions. While technology alone might not be the solution, it is a significant enabler from several perspectives.
Not only can technology make access and use of data faster, cheaper, and easier, but it can also contribute significantly to changing the mindset and behavior of organizations, pushing them towards embracing a data culture. Small steps in technology adoption can ignite a spark in organizations as they start to see the benefit in terms of faster processes and less routine work.
How we saw our clients become more data-driven thanks to our technology
At CARDO AI we empower our users to benefit from the data coming from their private debt transactions to its fullest potential, by providing all the necessary tools and technology to quickly access it, understand it and make decisions based on it. Using the right tools makes it easier for all teams across the whole organization to take advantage of the data they have and facilitates the development of a data culture.
Let’s consider a very quick example. Among the functionalities of our products, we have one completely focused on data quality. Our Data Health Check performs multiple checks within files and across them (i.e. consistency and reconciliation checks) in an automated way, with the goal of guaranteeing high-quality data. This means that the data provided by users to our platform has to go through an accurate set of controls before moving to the next step of the process. As users are aware they cannot afford to upload low-quality information, they become also more aware of the importance of constantly working with and producing reliable data. We have evidence of a tremendous improvement of the data standard, with errors in the files provided dropping by more than 90% (on new data points).
At the same time, once data quality improves, those who use data in the next steps of the workflow for their analysis or processes will be keener to rely on it, avoiding the usual doublechecks they did before and freeing up time they can better spend in other ways.
It looks like the saying “the best way to start is by giving yourself a start” (C.J. Walker) applies also to the data-driven approach. And technology can be a way to start, as it can create a positive loop that triggers the improvement of skills and abilities, and eventually supports a data culture.
Needless to say that the benefits of the technology on users and processes are clearly amplified by more complex solutions such as our Securitization Platform, which covers much wider uses cases and supports users throughout many of the monitoring, reporting, and analytics activities they have to perform on a regular basis, allowing faster actions and eventually supporting better decisions.