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Data Strategy: what it is, how to apply it, and why
In a world increasingly driven by data but dominated by great uncertainty, following a well-defined data strategy is critical for any company, regardless of size or the industry in which it operates.
In this article we first try to clarify what a data strategy is, what characterizes it, and what are the tangible benefits of a robust and arguably decisive data strategy for business development.
What is Data Strategy
More generally, we can say that the adoption of a data strategy represents an enterprise’s willingness to transform data into a key lever for innovation, improved decision making and the achievement of business objectives.
This can only happen within a well-structured framework that embraces data collection, data analysis, data management, data governance and finally data use.
All aimed at achieving clear and defined long-term strategic goals and in full compliance with values such as privacy, transparency, security and ethics.
There are at least three compelling reasons why companies should have a robust data strategy now more than ever.
First, increasingly stringent national and European rules promote responsible and compliant data management.
Without a structured and informed approach to data management, companies without a clear data strategy risk financial penalties and legal repercussions.
Second, the exponential increase in the volume and variety of data poses new challenges.
The enormous amount of data produced requires appropriate strategies for its storage, processing and analysis, without which companies would in fact be unable to take full advantage of the information potential available to them.
Finally, the importance of data-driven insights for informed decision making cannot be underestimated.
In an environment where business decisions need to be quick and effective, having a data strategy to understand and make the best use of information assets becomes crucial to maintaining a competitive advantage.
What are the benefits of Data Strategy
This strategic approach enables companies to identify operational gaps and take corrective action to improve the efficiency of their processes, enhance the experience of their customers, and find new drivers to support business growth.
In addition, a sound Data Strategy is essential for recognizing potential risks and uncovering breaches in their management.
It is critical for an enterprise to have a clear understanding of what data is collected, where it comes from, who can access it, how it is stored, how it is used, and for what purposes, as required by current regulations.
Implementing a robust Data Strategy represents a decisive competitive advantage for companies, which can, for example, understand the behavioral patterns and preferences of their customers and thus have critical knowledge to personalize products or services and increase their level of satisfaction.
In a nutshell, the benefits of implementing a Data Strategy are many and can be summarized as follows
- Efficiency
- productivity
- cost reduction
- risk management
- business opportunities
Efficiency increases when companies, having the right data, identify and optimize slow or inefficient processes.
Productivity improves because employees, having access to relevant information on customer preferences, market trends, production data, and logistics flows, are better equipped to do their jobs.
In addition, an effective Data Strategy can lead to significant cost savings by identifying areas of waste or under-utilization of resources and acting accordingly.
Another key benefit relates to risk management and compliance.
Here, well-defined Data Strategy can help mitigate risks associated with data breaches, regulatory noncompliance, and reputational damage.
Finally, access to real-time data insights enables companies to continuously discover new ways to improve products or services, identify emerging market trends and customer preferences ahead of competitors, thus ensuring innovation, scalability and revenue growth.
The Stages of a Data Strategy
We generally refer to seven constituent elements underlying a Data Strategy:
- data acquisition
- governance
- data quality
- access
- use
- extraction and reporting
- analysis
Many of these elements can be reconfigured to fit the organizational context and level of maturity of the enterprise, but all must be present in some form.
By integrating these components into its strategy, the company will enjoy the availability of data of sufficient quality to promote its strategic goals.
In general, we can say that to develop a Data Strategy suitable for the big data era, companies must first establish strategic objectives, assessing the current scenario and defining its governance.
Next comes the phase of designing the infrastructure and implementing the data platform, on which to then build analytical capabilities, strengthening security and privacy measures.
All this is done without neglecting either employee training and the creation of a dedicated team, or the subsequent stages of assessment, monitoring and verification.
But let’s look in detail at how a robust Data Strategy can be articulated, defining a roadmap with key steps
Vision
It provides a clear and compelling picture of how data will be leveraged to drive innovation, improve decision making, and achieve business goals.
The vision sets the direction of the data strategy, inspiring stakeholders and aligning their efforts toward a common goal.
It is important to align data use with organizational goals, building on a 2-3 year vision to determine the role of data in the big picture.
At this stage, it is critical to clearly articulate the “Why” of the strategy, which can be driven by the need to increase business visibility, monetize data, create new products, and increase efficiency in data use.
Stakeholders
It is also important to identify who are the key stakeholders related to data investments.
We are talking about both internal management, but also business functions and persono customers.
Each stakeholder must be considered from his or her own unique perspective on the organizational data initiative.
A successful roadmap requires alignment on this aspect as well.
Audit
Downstream from the auditing phase, the adoption of the right architecture and tools can be evaluated, selecting the right tools and platforms for the vision.
All elements should be considered, from data warehouses to connectors, from integration tools to reporting platforms.
It is important to choose the platform, to ensure a seamless flow of data between systems, while also evaluating aspects of usability, security, compatibility and scalability.
KPI
Organizations must monitor data quality, integration, storage and delivery by setting relevant KPIs.
Data strategy KPIs are the key performance indicators that measure the impact and results of your data strategy.
These KPIs must be SMART (specific, measurable, achievable, relevant, and time-bound)) and aligned with your business objectives.
Examples of data strategy KPIs are business intelligence KPIs such as customer retention or churn; data governance KPIs such as data quality score or data maturity level; data analytics KPIs such as insights generated or actions taken…
Governance
Establish clear accountability for data-related decisions and actions.
Including a data governance framework is a key component of the roadmap, as it provides a high-level view of initial configurations, row-level security, dataset certification, and regulatory requirements for sensitive data.
Organization
Also part of this phase is the analysis of the skills needed to execute the strategy based on the selected goals and tools, assessing whether these roles already exist in the organization or need to be created.
Implementation
Monitoring, Review and Update
It is a strategy that falls squarely within that “continuous improvement” philosophy that the current technological and market scenario demanded.
And only then can the strategy succeed in being relevant, efficient and flexible.
How to implement the Data Strategy
We have seen how the basis of developing an effective data strategy must be a clear understanding and definition of objectives, aligning them with the company’s overall strategy. Only by keeping Data Strategy and strategic vision aligned can a tangible and measurable return on investment be fìgured.
Accurately identifying data sources is the next and crucial step.
Data sources, which can include customer, sales, and financial data, must be categorized and evaluated for quality and relevance.
This process enables a holistic approach to data management, ensuring that it is accurate, complete, consistent, and free of bias. Equally crucial is the establishment of clear and detailed data governance policies that ensure data quality, security, and compliance.
Such policies should precisely outline stakeholder roles and responsibilities, establish quality standards, and define processes for data management and access.
Of course, you then need the right tools, just as you need the right involvement of everyone involved, both in terms of training and in terms of understanding the strategic path forward.
Developing data analytic capabilities tailored to the specific needs of the business requires the selection of appropriate and scalable analytic tools and technologies that enable the integration of data from multiple sources into a single repository and rest on an appropriate and secure architecture.
