Look at the biggest names in the digital space – Google, Facebook, Amazon. A common factor in their success stories is how they have aced at connecting with their users, knowing their preferences, browsing habits, topics that interest them, and how long they are most likely to interact with that specific topic.
These titbits of customer information have played an immeasurable role in the success of leading internet companies. They have inspired changes across levels, redrawing strategies and fine-tuning approaches to create products and services that are more personalized and relevant to the customer’s needs. Their one defining mantra is that a satisfied and happy customer will definitely return for more!
What is data maturity?
In simple words, data maturity is the process of improving and enhancing capabilities in utilising data.
Data maturity is a measure of how developed an organization’s data analysis and integration capabilities are. In other words, a company that has seamlessly integrated data into their culture takes data-driven decision. These organization have reached a high level of data maturity.
Some of the best examples of superior data maturity are early adopters like Netflix, Uber, and Airbnb. These companies are data innovators and leading players in their respective industries. These organizations have given so much priority to data that sometimes, they appear more as data companies than brands in the entertainment, transport or hospitality industries.
From addressing evolving consumer preferences to launching pioneering a range of products and services, companies are amplifying the ability of data to make game-changing decisions.
Way back in 2014, McKinsey pointed out that intensive users of customer analytics are 23 times more likely to outdo their competitors in acquiring new customers than non-intensive users. They added that intensive data users are nine times more likely to scale past non-intensive data users in customer loyalty.
According to a report by leading market intelligence firm IDC, as businesses lookout for solutions to facilitate quicker and better decisions, it has resulted in healthy spending on big data and business analytics (BDA) solutions across all industries. Moreover, using data for generating valuable insights across the board, from internal business ops to the customer, is now of strategic importance to organizations across diverse sectors. IDC has pegged the compound annual growth rate (CAGR) for global BDA spending at around 12.8% for the 2021-2025 forecast period.
But what does data include?
For a business, data refers to all information collected by the organization. It includes information about the customers — who they are and what they purchase, the services they use, the marketing initiatives they interact with etc. It also consists of the financial information of the business (profits, payroll, revenue, expenses), staff information, supply chain information, HR, inventory, and R& D data, in one business management system.
If all this data isn’t organized systematically, it’s just a pile of numbers and text lying around the place. Data maturity is not measured by the amount of data you have; but by how efficiently you have processed, analyzed and used your data to make sound business decisions.
So the more advanced methods and technologies a business use to interpret the data, the greater its data maturity.
Indicators of data maturity
Organization culture and leadership approach
To create a thriving data-driven culture, leadership teams in organizations need to focus on data and adapt business models accordingly. Moreover, the inclusion of a chief data officer (CDO) supported by an able team of data analysts, data engineers, and data scientists helps centralise and extend the scope of data across the business strategy. Naturally, this will help achieve the stated goals.
Rather than confining data-related activities to the IT department; the leadership must integrate data awareness and data-driven decisions across all functions and involve the entire organization.
Develop effective DataOps
A company needs to implement the appropriate technology stack if it wants to be data-driven. Cloud-based environments offer various services for processing data, and data-oriented companies like Netflix use these effectively. The services included storing and querying data, managing applications, layering MI algorithms on the data collected to run advanced analytic models, and data visualization dashboards.
For DataOps to be successful, the data in the network should be reliable. Companies should audit their data before investing in the digital infrastructure to process it. The value lies in the infrastructure that extracts data insights.
Prioritized investments in data and technology
There should be a proper allocation of hardware and software components budgets and continuous training for human capital. Skewed budgeting either has digital systems that most don’t know how to use or a highly literate workforce with no supporting infrastructure to leverage the business data.
Further, organizations looking to become data-driven often invest in big data analytics and machine learning initiatives to gain more insights from the enterprise data. It’s likely you may also incorporate data governance initiatives to comply with regulations and improve the decision-making quality.
Continuous training and development
One of the biggest hurdles to a robust data-driven workforce is the talent skill gaps.
Data experts struggle to convey their interpretations to stakeholders resulting in underutilization of the information. A data-driven company must implement regular upskilling programs to enhance the data literacy of the employees.
Ability to use automation
An indicator of a company’s digital maturity is its ability to use automation efficiently, decreasing costs and enhancing output. Organizations can attain this by introducing stability in its people, process, technology, and data.
Top reasons why businesses need to become data mature
Future-ready and able to spot opportunities and threats
Data mature organizations can leverage predictive and prescriptive analytics. They can use existing data to predict what will happen in the future.
For example, companies can forecast which candidate is most suited for the job before signing the contract. Companies will also be able to pre-empt likely threats such as employees who are most likely to leave the company. They can also find ways to reduce time-consuming processes, and predict which months of the year are likely to be bad for sales. Having this information in advance enables employers to have a plan to address these issues; and tackle these eventualities whilst ensuring there are no surprises.
As organizations move toward data maturity, they may even begin implementing artificial intelligence and other superior technologies to secure more insights from the available data. This is an even more advanced stage where issues are highlighted even prior to being considered.
Efficient use of automation indicates a data-driven culture with minimal human intervention. It reflects the stability of the critical components of the business, including people, process, technology, and data.
A data mature organization can decrease costs and improve performance. It creates a leaner and more flexible workforce, making the organization resilient to economic shocks.
Ability to use dark data
One of the critical advantages of data mature organizations is their ability to identify and utilize dark data. This basically includes unquantified, siloed, untagged data sets that are a side effect of sprawling systems and missing metadata. These data that does not make any sense is called as Dark Data.
Organizations with more advanced capabilities to find data and then implement it outperform their less data-mature competitors.
Businesses worldwide are feeling the heat as markets shrink and competition gets tough.
Differentiation is essential for organizations to stand out and get ahead of the pack.
Most companies realising this have begun utilising existing data to improve core processes and operations and, at the same time, also launch brand new and enhanced business models.
Enhanced data maturity offers data-driven businesses valuable insights that drive up the momentum whilst enabling these companies to chart new territories confidently, shaking off doubts about limitations that would otherwise keep them from growing the business.
A survey by MIT and IBM revealed that businesses with a high level of data maturity witnessed higher sales per employee, higher operating income and higher sales growth too.
Advanced data maturity is becoming a game-changer in many industries, and data-driven organizations are capturing the lion’s share of the market in some very highly -competitive markets.
Higher Returns on Investments
Businesses that utilise analytics benefit from improved sales, an excellent perception of customers, and opportunities for innovative products relevant to their needs. The businesses can also make more accurate forecasts of financial performers. And, have a better awareness of the key financial drivers.
As data maturity progresses, from descriptive (What happened?) to diagnostic and then predictive and prescriptive to finally reach the cognitive level (What can be done?), you add more information and complexity.
To sum it up, data maturity improves ROI by using effective data management.
A survey finding by Deloitte highlighted that nearly half of all respondents in the survey stated that one of the most significant benefits of using analytics was improved decision-making capabilities. In the same study, 16% reported that its most important benefit was enabling better strategic initiatives. Nearly two-thirds said that analytics capabilities played a crucial role in taking the business ahead.
In essence, data mature organizations can take facts-supported decisions. They do not use intuition to forecast whether a customer will be interested or not in a new product. Instead, they can rely on available data to formulate a suitable decision.
Better connected businesses.
When inter-departmental and cross-functional areas in organizations don’t share information as frequently as required, departments become inward-looking units, losing focus of the organization’s overall vision.
Unfortunately, silos are typical in many businesses and in such cases, data management and sharing go for a toss throughout the company. When data goes out of sync, localised and disconnected decisions are taken.
Many data-mature businesses have adopted a centralised data store, incorporating clearly defined data standards. This facilitates a clear overview of the organization, and activities in different business areas are aligned to be in sync.
Data mature organizations can efficiently tackle organizational and technological silos, making it convenient to unlock new insights and information.
Organizations – big and small – have realised that merely investing in IT is not good enough to create a data-driven business. Data maturity offers businesses, irrespective of their size and scale number of advantages based on the efficient use of data.
Moreover, every organization generates data. It could come from the web, our mobiles, online and offline payment systems, surveys, social media and other sources. Data is fast becoming a critical asset for organizations, with some even touting it as the currency of the 21st century.
There are numerous online tools available to assess your organization’s data maturity. MData maturity can add an entirely new dimension to your business, not just with how it utilises existing data but also in how it functions.