How to Formulate a Comprehensive Data Strategy

In the age of digitalization, data is like the new oil. But extracting value from data over the long-term requires a strategy – and this post will help you establish one. We’ll cover why data strategy is important, how data strategy supports business strategy, and the 9 essential elements your data strategy should contain to be successful. Let’s dive in.

This article is part of our blog series called The Road to Data Governance. If you’re joining us for the first time, you’ll want to read Part 1: The Data Maturity Assessment for an introduction to data governance.

 

 

Why data strategy matters: 3 key business benefits

A 2019 McKinsey survey made it undeniably clear that businesses that implement and adhere to a long-term data strategy outperform competitors that don’t. The reasons boil down to the impact of data strategy in three key business areas: decision-making, competitive edge, and operational efficiency.

 

1. It helps you make smarter business decisions

The ultimate goal of data strategy is to improve your ability to solve problems on the basis of empirical data that you’ve collected, organized, and analyzed. A data-driven approach not only leads to better business outcomes and improved efficiency, but also aids in monetizing data stores and improving compliance. But this comes with a caveat: in order for data strategy to help achieve business goals, data initiatives must be defined and prioritized on the basis of existing business strategy.

 

2. Discover new competitive edges  

Once a data strategy is in place, you can identify trends, customer preferences, and market opportunities you would otherwise miss. Plus, advanced technologies like AI and machine learning can magnify your advantages by predicting future trends and recommending strategic courses of action in both the long and short term.

 

3. Improve your efficiency and profitability

A comprehensive data strategy can significantly improve your operational efficiency – especially at the enterprise level. From supply chain management to sales, manufacturing processes to human resources, the insights you glean from your properly managed data stores can pinpoint inefficiencies, uncover hidden opportunities, and pave the way for sustainable improvement.

 

 

The 3 pillars of data strategy

Implementing a data strategy means initiating changes at three different levels of an organization.

 

People

Just as you can’t implement a marketing strategy without a marketing team, you can’t implement a data strategy without first assembling a data governance team.  But not only do you need the right people, you need the rest of the organization to understand and support their work. Getting that buy-in requires organization-wide training to boost data literacy, which may take the form of workshops, online courses, or seminars that keep your team agile and informed in a rapidly evolving data science landscape.

 

Process

For CEOs and CDOs wondering where to start, establishing an effective data governance framework is key. When you combine that framework with quality assurance and lifecycle management, this foundation ensures your data is as useful as possible. AI-driven tools and automated workflows can help to further streamline the process, enhancing data quality and governance efficiency.

 

Technology

Technological solutions, ranging from day to day analytics software to data architecture optimization to robust data security implementations are integral to a winning data strategy. Keeping an eye on emerging trends, like edge and quantum computing, will ensure your organization remains at the cutting edge of data management practices.

 

Technology Partner: Informatica

Support data strategy initiatives by choosing the right technology partner. Informatica stands out with its Intelligent Data Management Cloud (IDMC) platform, blending seamless data integration and governance with AI-enhanced analytics. Opting for Informatica as your technology ally ensures your data strategy is not only comprehensive but future-ready.

 

a table with a spread of various documents and charts
A data strategy can take the form of a document, presentation deck, or dashboard

 

 

What elements should a data strategy contain?

 

Creating a data strategy isn’t a task you can simply check off your to-do list — it’s an iterative process that usually takes several phases before it matures into a vital component of your overall business strategy. The strategy itself can be presented in several ways. It may take the shape of a document, a presentation deck, or a dashboard. Regardless of format, it’s imperative that it addresses the following sections (and not necessarily in this order):

 

1.A stakeholder engagement plan

In this introduction section, CEOs, CTOs, or any other members of the governance council make the case for the importance of a comprehensive data strategy to get the rest of management on board. It’s important that this section explains how you plan to use your data, and includes a proposal for how data owners should disseminate information and define roles and responsibilities to their teams.

 

2. Goals & KPIs

This section should outline business objectives and suggest data governance metrics to gauge both the progress of your DG strategy and its impact on your business goals. These could be financial indicators (e.g., ROI, cost savings), operational measures (e.g., data processing time), customer-related metrics (e.g., retention, satisfaction), employee metrics (e.g., time per project), governance metrics (e.g., data breaches, compliance issues), etc. Chosen metrics should be actionable and aligned with the business strategy you cover in the stakeholder engagement plan.

 

3. Implementation plan

This is where your strategy turns into a concrete plan. Be sure to include estimated phases and timelines, as well as a deadline for reaching full-fledged data governance. You should also include a list of defined roles and expectations, a detailed list of resources needed, and a financial overview with projected costs and allocation plan.

You’ll want to consider expenses like labor costs, resources, data literacy training, technology and tools, consultant fees, and so on.

Finally, include a communication plan with key messages, FAQs, and a template for sharing outcomes of your data governance efforts with the rest of the team.

 

4. Data governance framework

This serves as the overarching structure that outlines the fundamental objectives, principles, and processes for managing and utilizing data within an organization. It provides a high-level roadmap that defines roles and responsibilities and the core components of the data management system (like data classification, data lifecycle management, and data quality).

The framework acts as a guideline for how data should be handled across the organization, directing the development of specific policies and procedures that align with the defined objectives and principles.

 

5. Data governance policies

You should come up with an initial list of specific guidelines and protocols derived from the DG framework to regulate the day-to-day management of data. These policies provide detailed instructions on how to carry out various data-related tasks, such as data access, security, privacy, quality, and compliance.

Data governance policies delve into the specifics of data handling, outlining the steps and procedures that employees need to follow to ensure data integrity, security, and compliance with relevant regulations. They serve as the guidelines that operationalize the broader principles set forth by the data governance framework.

 

6. Data architecture

The data architecture section serves as a blueprint for the way that data flows through an organization. In this section, you should provide a detailed breakdown of your existing data assets and infrastructure, which includes databases (from simple spreadsheets to complex relational database management systems like MySQL, PostgreSQL, or Oracle), data warehouses, and data lakes. It should also include an integration strategy that states how you’ll connect disparate systems and enable smooth data flow across verticals, and an interoperability strategy that states how these disparate systems can understand and utilize each other’s data.

 

7. Data quality management

This section should outline processes for ensuring high-quality, reliable data across the various data domains in your organization. It should encompass data profiling procedures for identifying anomalies and inconsistencies, data validation procedures to ensure data accuracy, and data cleansing procedures to eliminate errors and redundancies. This section should contain both an overarching overview of quality management principles plus domain-specific guidelines as necessary.

 

8. Data analytics and business intelligence management

To prevent uncontrolled analytics practices, it’s important to standardize the way data is captured, analyzed, and transformed. For this reason, your data governance strategy should include a section with actionable procedures that will ensure your business intelligence efforts are consistently applied organization-wide. This might include listing approved tools or software; programs for data entry, mining, visualization; permissions for access and distribution, and outlined processes and best practices.

 

9. Change management and training  

Here you should outline a strategy for staying current with evolving data science and training programs for promoting data literacy across the organization. Include the names and providers of training programs you propose, proposed training frequency, and a system for employee training signups. Keep in mind that your team may have varying levels of data literacy, so you may also want to include a plan for training personalization and monitoring.

 

Informatica: Igniting Strategy for Data Revolution

 

Transform your approach to data management by integrating Informatica into your data strategy core initiatives. Informatica is more than just a technology provider; it’s a catalyst for aligning key aspects of your plan, from enhancing stakeholder engagement to refining data governance policies. This integration isn’t merely functional—it’s transformational, providing the technological foundation critical for executing a effective data strategy.

 

As you navigate the complexities of data governance, envision Informatica not just as a tool, but as a strategic partner. Their cutting-edge solutions are designed to empower your business objectives, driving a data strategy that’s not only robust but also technologically advanced and future-proof. Choose Informatica, and take a leap into a realm where data isn’t just governed – it’s mastered.

 

Help your business become the best version of itself

As digitalization transforms the way business is done around the globe, you need to prepare your organization with a strategy for capitalizing on digitalization’s biggest byproduct: data.

A comprehensive data strategy is an evolving blueprint that, when implemented properly, promises undeniable competitive advantages, huge gains in efficiency, and transformative business growth.

If you’re ready to get started on your journey to data governance, Poslovna Inteligencija can help.

 

 

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Business & Data Analysis
Planning, OLAP & Reporting & Financial consolidation
Data Integration, Data Migration & Data Engineering
Data Governance, Data Quality & Master Data Management
Data Science
DWH models
XBRL point
ConQ Content Analytics
SynQ

I acknowledge that the personal data I submit through this contact form will be used by Poslovna inteligencija d.o.o. to contact me and provide information related to my inquiry/application. I consent to Poslovna inteligencija using my submitted personal data to send newsletters containing information about news, products, and services of Poslovna inteligencija. I also acknowledge that I can unsubscribe from receiving newsletters at any time by clicking the 'Unsubscribe' link in each newsletter. Comprehensive information related to my rights and the use of my personal data can be found in Poslovna inteligencija's Privacy Policy.:
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