It is nothing new to say that data is the oil of the 21st century and organizations have realized the relevant nature of data in the value chain of organizations, even beginning to give it the category of asset (strategic, business…).
However, although organizations have already taken important steps in information management, it is necessary to sow a series of “basics” to extract all the potential that data has in the business and embed that management in the business itself.
Empowering business units and giving them the necessary tools for self-consumption and self-management of information will be the main trends for 2024.
Data Governance into Operations
It is still very common to find organizations with a defined governance model (on paper, and generally very ‘standard’) without being operationalized and without being embedded in the daily work of business users.
One of the main reasons why we find many governance programs that do not have the expected success is that the governance model defined on paper does not adapt to the reality of the company (due to the lack of existence of profiles with certain technical capabilities. -functional, whether due to organizational culture, heterogeneity of business units, etc.).
Defining a data governance model tailored to each organization, providing value to business units in the short term from data governance and communicating clearly and effectively all the advantages of implementing governance on a day-to-day basis are some of the key activities that must be worked on to begin the path to the Operationalization of the Governance Model
Data Democratization
Today many companies aspire to true “data democratization” throughout their organization. Probably the most common goal of all CDOs and data leaders along with ‘self-service’ analytics or ‘self-consumption’, which are more or less the same concepts, data democratization is much broader than these.
Data democratization refers to the process of making information and data available and accessible to a broader public, rather than limiting its access to a select group of people or departments in an organization. This concept seeks to empower more people within an organization by providing them with access to meaningful data and the ability to use it to make informed decisions.
Currently, the lack of data democratization can be a notable problem that can affect operational efficiency (blockages due to dependence on other teams), inconsistencies (same information representing different things) and lack of knowledge (if I don’t see it, I don’t believe it). However, the challenge is not to make information available to everyone, but to do so in a controlled and governed manner, ensuring access and consumption of information to the people who need it and ensuring that they treat it appropriately.
In this way, the democratization of data has the intention of seeking to eliminate the barriers that exist when consuming information and ensuring that more and more people in the organization consume the information generated and base their business decisions on the analysis of data. But doing it with a business sense, generating knowledge in the data that is managed, data driven culture and providing tools so that the owners of the information fulfill their responsibility.
Data Quality focused on business
We talked before about the need to operationalize data governance, and data quality is the main tool to enable those involved in the Data Governance Program to perform their functions. We continue to see in many organizations how data quality is focused solely on technical users and is not given an appropriate business focus.
Defining and implementing a Data Quality Program associated with business profiles is essential for making informed decisions and ensuring that the information handled has a high impact on daily business processes.
Have a Data Quality Program understood, adopted and consolidated in the business so that reports, analysis and decisions are based on the reality of the organization and not on impressions based on previous experiences.
Sustainability
Companies that adopt sustainable practices demonstrate their commitment to corporate social responsibility, which can improve their image and reputation.
Modern consumers are increasingly concerned about the environmental and social impact of companies, and favor those that show a commitment to sustainability.
The implementation of sustainable practices can lead to greater operational efficiency, reduce long-term costs, improve brand image and much more. This may include energy efficiency, waste reduction and the responsible use of natural resources, more efficient use of energy or the use of green energy. Governments and regulatory bodies around the world are implementing regulations and laws that require companies to operate more sustainably, such as the famous ESG regulations.
Correct data management is crucial to face the new reality, and there are many reasons why Data Management must be taken into account to achieve the sustainable objectives that organizations set: collect and analyze data related to their environmental and social performance to meet disclosure and regulatory requirements, transparently demonstrate your sustainable practices, which can be crucial to comply with regulations and build trust with stakeholders and customers.
The value of Data Products
Many organizations have already made great strides and have spent a long time striving to have data products on which they can make decisions. However, in many cases, it is not possible to quantify the value that these data products are offering to the business teams on which they focus.
A current trend is to quantify the impact that data products are having on organizations: reputational value, increased revenue, cost reduction, time reduction…
Another fundamental aspect when analyzing the value of a data product is the concept of data effort. What does that mean? This concept refers to the potential cost savings that can be obtained in other data products thanks to having previously implemented one and that must also be measured.
We help our clients give a business sense to the data products and initiatives that are deployed and we quantify the value they are providing by evaluating the current, previous and expected situation. In addition, we propose continuous monitoring over time by implementing a methodology that allows us to know which initiatives have the most value for the organization and that allows us to make decisions about future work to be carried out.
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Data management associate at Keepler Data Tech, certified in DAMA (CDMP). I have developed my professional career in the field of Data Governance, Data Quality and Data Management in general.
"Squeezing all the business potential that data has is my main objective when we face a new project with my clients"
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