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Data Strategy
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5 Key Elements of a Data Strategy

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Where there is a business, there is data.

Every business has a treasure trove of data on its table but not every business is making money with data. Data is at the heart of businesses, whether it’s a small shop tracking their manufacturing and stock levels or a multi-national company predicting market trends and customer behavior.

While many companies recognize that data is a strategic asset, many are not able to capture its full value to forge ahead. Throughout this blog, we’ll learn about data strategy, its key elements and how an effective data strategy proves beneficial in making informed business decisions.

Even as organizations invest huge amounts of money in data analytics consulting platform initiatives than ever before, inefficient data management practices, lack of actionable insights, and siloed data continue to block the road to tap your data’s potential. A robust data strategy framework helps organizations overcome obstacles and pave the way to become more data driven. Moving further, let’s learn about data strategy.

What is a Data Strategy?

A data strategy is a long-term guiding plan that defines the processes, technology, and people involved in addressing your data challenges and supporting your business objectives. Creating a successful data strategy requires business leaders to deliberately examine their company’s operations through the lens of data and forecast what needs to happen to achieve the desired business outcomes.

It is imperative for business leaders to consider the following things while building a data strategy:

  • Which technology will enable data analysis, storage, and sharing?
  • How to ensure that data is easily accessible and of superior quality.
  • What employees require to make effective use of the data.

Why does your business need Data Strategy?

Having data is not enough. As a famous author, Bernard Marr quoted “doesn’t matter how much data you have, it’s whether you use it successfully that counts.”

To make the most of your data, you need to have a strategy in place in order to achieve outcomes aligned with your business goals. With a well-defined strategy, you can set your organization to be innovative, business users to be effective, and business to remain relevant and competitive even during unprecedented times.

Without a data analytics strategy, you are bound to experience common data challenges that include:

  • Inability to make data-powered decisions in real time
  • Reporting on the past and not forecasting future needs
  • Low user adoption of advanced technology
  • Inconsistent, poorly defined definitions for KPIs and metrics
  • Data stuck in silos and departments
  • Manual data integration and its complexities
  • Expensive raw data preparation
  • Data quality and accessibility issues
  • High dependency on IT

A data strategy serves as a foundation for your data practices and enables your business to remain agile while gaining a competitive edge.

Assess your data spectrum with our data discovery workshop to understand your business’ analytics needs and get personalized data analytics solutions.

Our Key Elements of Data Strategy

Polestar Solutions has helped organizations make better use of data by creating a robust data strategy and leveraging analytics. Our extensive experience and industry-proven approach has resulted in the key elements of data strategy as listed below:

Five Key Elements of a Data Strategy - infographic Photo

#1. Alignment with Business Needs and Goals

Data practices must address your business needs so that you can extract real value or else you might fall prone to chasing wrong resources, irrelevant projects, unactionable insights, and even loss of trust in data initiatives throughout the organization.

When you tie your data strategy with your business goals, you set your organization for success. You can prioritize data activities that underpin new growth opportunities and unleash unmatched value.

We’ve listed down a few ways to align your data strategy with your business goals:

  • Determine key business drivers that could be impacted by data and analytics.
  • Understand departmental activities and how they sync with business goals.
  • Know your existing IT capabilities and technology gaps.
  • Compare your results to industry norms and determine how your organization’s data serves each business and which areas are missing out on crucial data-backed insights.

#2. Data Maturity Evaluation & Analytics Techniques

Wise words by Henry A. Kissinger, “If you do not know where you are going, every road will get you nowhere” ring true here.

It is crucial to know your starting point, your existing analytics maturity level, and what your future analytical needs are to set attainable goals and take a realistic approach to become data-driven.

A Gartner study says that modern analytics fall into four different categories: predictive, prescriptive, descriptive, and diagnostic.

You need to have the following to view a full picture of data maturity and analytics:

  • A list of the equipment, innovations, and systems you currently employ.
  • A thorough description of your data infrastructure and current data architecture.
  • A review of organizational procedures and employee capabilities in relation to data and analytics.

To fulfill business objectives across the organization, you can determine where you have gaps, where there are challenges, and what you need to optimize - whether it be technology, processes, or people.

As you develop capabilities and carry out tasks from your data strategy, your data and analytics maturity level can be used as a tool to prioritize your projects and as a benchmark to track your progress.

Ready to take your data strategy to the next level?

Check out our free e-book for expert insights and actionable tips on leveraging AI in your organization's data strategy.

#3. Data Governance

Establishing your governance model is crucial as you create your data strategy. You will have a more effective plan if you know what data you're seeking to govern, who is involved, and how it will be governed.

The duty for data governance is often delegated to non-business staff members, such as those in information technology (IT) or data teams, which is a mistake we frequently see organizations make when attempting to establish their data strategy without the knowledge of Data Agility. The issue with this is that they are there to provide data governance support for the business.

Instead, the business data owners must oversee governance. Among other things, they must accept accountability for their data.

#4. Collaboration

Data utilization in modern enterprises is often more collaborative than in the past. More people may participate in analytics as well as technical areas like data preparation and data quality as a result of improved data literacy and more user-friendly tools.

Crowdsourcing can be used for even highly regulated procedures like data governance and the creation of master data definitions. In a manufacturing organization, for instance, it can ensure that product names, error codes, and management procedures accurately match the reality on the shop floor.

There's no code for that, the most infuriating customer care response, can be avoided through collaboration on master data.

Think about how data and analytics will influence your organization's business decisions, and search for mechanisms that encourage collaboration both within and outside of teams. Utilize this knowledge to assist in the sharing and commenting of reports, dashboards, and data visualizations.

One such feature allows multiple users to annotate visuals in various BI and analytics systems. They increasingly interact with messaging and chat apps. When enabled by enterprise-class scalability and security capabilities, even basic file sharing can prove to be productive.

#5. People of Organization

Data and people are the two key components of your data strategy. In new business recruits, organizations are aggressively looking for data literacy and basic analytics skills.

Data scientists are in high demand at data-driven organizations. Since every data science course gets fully subscribed, the market will most certainly glut us with qualified candidates in the upcoming years. But just now, it plays a crucial part.

When hiring and employing, you should also give IT and data management significant consideration. It's tempting to believe IT only needs to keep the lights on because there is so much technology operating in the cloud and systems are more reliable than ever. It is NOT true. It is responsible for high availability, disaster recovery, service level agreements, supporting new business requirements, and meeting regulatory standards.

In order to address business needs, data architects, data integration developers, data engineers, database administrators, and other data management specialists play important roles.

A strong strategic advantage is having an IT workforce that possesses industry expertise. As much as any other position, it requires acknowledgment and support from the leadership.

Getting Started with an Effective Data Strategy Implementation

The aforementioned five key components of a data strategy are a perfect roadmap to address both existing and future data needs however it is crucial to consider the broader concerns like marketing plans, industry competition, business budgets, staffing policies, legal standards, etc.

Understanding the strategic objectives of your entire firm is necessary before you can implement a data strategy. Define the function of data and how it will be used and managed next, and then consistently implement it in the production, finance, marketing, HR, and other departments.

The outcome will be a data strategy that is adaptable to the shifting demands and pressures of business.

At Polestar Solutions, we optimize data analytics services to help organizations maximize revenue, unlock new growth opportunities, and enhance customer experience.

If you would like help creating an effective own data strategy and its implementation, get in touch with our Experts today!

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