What Is Data Strategy?

Data Strategy defines a set of choices and actions that, together, define a high-level course of action to achieve high-level goals. It involves business plans to use knowledge to a competitive advantage to support business objectives. A Data Strategy requires an understanding of the data needs inherent in the Business Strategy.

Like many other terms, “data strategy” has several synonyms in data management. They include but are not limited to business data strategy, business data management strategy, information management strategy, business information management strategy, information strategic plan. All these terms refer to the same concept of a single, enterprise plan for the use of organizational data as an essential asset for strategic and operational decision-making. A data strategy defines the approach that the enterprise will take to manage and use its data and information to achieve its business and technology goals and to realize a competitive advantage using this asset. The concept behind designing an information strategy is to ensure that all services are placed in such a manner that they can be accessed, exchanged and distributed easily and efficiently.

Data is no longer a by-product of business processing – it is a critical asset that enables processing and decision-making. A data strategy helps to ensure that data is managed and used as an asset. It sets out a common set of objectives and objectives across projects to ensure that data is used both efficiently and effectively. The Data Strategy sets out common methods, practices, and processes for managing, manipulating and sharing data across the enterprise in a repeatable manner.

Strategic data Management is a methodology that fits within the general information engineering umbrella, addressing two critical phases of information engineering: organizational analysis, and the strategy-to-requirements transformation. When businesses experience digital transformation (DX), they are shifting toward new business models that identify information as a key strategic tool to control apps and drive business decisions. To do that, it is important to turn the data that companies generate and maintain into a strategic capital asset referred to as ‘data assets’. Modern information technology (IT) systems and workloads are not designed to drive the development of data resources. The size of data that must be collected, processed, secured and made available for use, as well as the demands of next-generation applications (NGAs) that IT companies are creating to drive software resource growth, goes beyond the capability of conventional systems in the areas of efficiency, scalability, reliability, mobility, and manageability.

Moving to data-centric business models is designed to enable organizations to turn their data into data capital. According to the more rigorous efficiency, scalability, reliability, mobility and governance criteria of the information-centric paradigm, these organizations will modernize their processing and data protection architecture through the IT transformation process. Most large organizations now consider that, although they can obtain data from multiple divisions, the lack of logical software alignment through information systems makes it difficult or impractical to address cross-functional or cross-divisional questions. This through their ability to take advantage of potential opportunities or to react to market challenges. To overcome this challenge Strategic Data Management and planning are used. It is a formalized, top-down, data-centric design methodology that constructs an organizational structure, its roles, structures, and underlying data as a framework for defining and integrating an interconnected collection of information systems that addresses business needs.

A well-defined data strategy can help you achieve the following:

  • Align all aspects of the enterprise to a single-minded “information” target that everyone recognizes.
  • Serve as a focal point for individuals to connect with the purpose and direction of an agency.
  • Direct daily activities by delivering a constant, clear message as to what is relevant.
  • Determine priorities and encourage decision-making.
  • Establish the guiding principles by which data is operated.
  • The purpose of the data efforts to external stakeholders is clarified.
  • Articulate that people are going to and will not spend time.
  • Provide a basis, or standard, for allocating organizational resources.
  • Facilitate the translation of strategic objectives into organization structure, capabilities, team organization, team composition, and work processes around data.

Many organizations have already engaged in data management programs across different components; sadly, the different areas are not typically organized or synchronized. Data management issues for the company demonstrate how the absence of a Data Strategy can cause significant difficulties in obtaining and using information. A Data strategy provides insight into the connection of each of the elements (or disciplines) to each other. If you’re not organizing the different component tasks, you risk providing a lot of point approaches that can’t work together.

The Power of the Data strategy elements is that they allow you to identify a specific, concrete target in each field of discipline. Taking into account, a feasible Data Strategy begins with identifying the strengths and weaknesses that exist within a Data environment and identify a workable and measurable set of goals which will improve Data access and sharing. The value of a Data strategy is to deliver the best possible solution according to the changes in the organization’s requirements. Data Strategy plan is a road map and a way to meet all existing and future data management needs.

Data Management vs. Data Strategy

As per the Management Book of Knowledge 2.0 (DMBOK2), Data Management is: “The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”

As per the DMBOK2, Data Strategy is: “Typically, a Data Strategy requires a supporting Data Management program strategy – a plan for maintaining and improving the quality of data, data integrity, access, and security while mitigating known and implied risks. The strategy must also address known challenges related to Data Management.”

A Framework for Understanding Data Management vs. Data Strategy Needs

Donna Burbank presented a five-level model designed to help her clients understand the relationship between strategic planning and data management, as well as explaining areas where the enterprise may need to evolve to use data strategically and efficiently as possible, as shown in the figure below.

A Framework for Understanding Data Management vs. Data Strategy Needs

A framework will allow an organization to identify gaps in key areas that need to be resolved before going ahead. The trick, Burbank says, is to find the easiest thing to do with the greatest benefit, which will be different for each organization.

Level 1: “Top Down” Alignment with Business Priorities: Data Strategy

At this level Business Strategies are aligned with Data Strategies.

Level 2: Managing the People, Process, Policies, and Culture Around Data: Data Governance

Data Governance provides a framework for managing people, systems, strategies and information culture. The sophistication of an enterprise at this stage – or lack thereof – will decide the opportunities you have for utilizing the Data strategically, as well as the timeframe for putting it into practice.

Level 3: Leveraging and Managing Data for Strategic Advantage

This level includes the various Data Management practices that help leverage data for strategic advantage, such as Data Quality, Master Data Management, Data Warehousing, and others.

Level 4: Coordinating and Integrating Disparate Data Sources

Data Integration comes many different questions that need to be asked and answered: Where are all those data sources? What is the inventory of all those sources we need about our customers? How do we know where it is and where it should be? How do we integrate all the different formats? How do we understand it and get the lineage through metadata?

Level 5: “Bottom-Up” Management and Inventory of Data Sources

Relational databases, Big Data, unstructured data, XML, documents, voice, and media, so how do you make sense of that? These disparate sources can be used to inform Business Strategy.

Components of a Data Strategy

To be successful, a data strategy must involve each of the different disciplines within the framework of data management. The data strategy must address data storage, but it must also take into account how data is identified, accessed, shared, understood and used. Only then will it resolve the problems related to making information available and functional so that it can help today’s multitude of storage and decision-making practices.

There are five core components of a data strategy that work as building blocks to comprehensively support data management across an organization:

Components of a Data Strategy

1. Identify

One of the most basic building blocks for the use and exchange of information within an organization is the creation of a way for defining and describing the material. The storage and processing of information, whether structured or unstructured, is not possible unless the data value has a name, a fixed format and a meaning representation (even unstructured data has such details). Such information should be regardless of how the data is stored It is also important to have a reference and access method for your data metadata. Consolidating business terminology and meaning into a business data glossary is a common way to address part of the challenge.

2. Store

Most of the organizations have advanced methods for identifying and managing the storage needs of Data, each system receives sufficient storage to support its processing demands.

The bulk of companies use sophisticated methods to schedule resources and assign space for different systems. Unfortunately, this approach reflects only the “data creation” perspective. It does not cover the sharing and use of data.

The flaw in this strategy is that there is never a framework for efficient storage management required to transfer and pass information between systems. The reason is simple, the most obvious exchange of information in the IT environment is transactional in nature.

Most of the information which is shared between two organizations fall into two categories: internally created data and externally created content. The absence of a structured data sharing mechanism usually requires all organizations to handle this area independently, so that everyone generates their own version of the origin.

When companies have developed and information holdings have increased, it has become apparent that holding all data at a single location is not feasible. It’s not that we can’t build a platform that’s large enough to hold the information. The key is to make sure that there is a reliable way to store all the information that has been generated in such a manner that it can be easily accessed and distributed.

When information is generated, it will be exchanged with a variety of other systems; it is important to approach space effectively in a manner that simplifies access. A good data policy would ensure that any content generated is available for future use without forcing every one to produce their own copies.

3. Provision

Most of the application systems have been built as individual, independent data processing engines containing all the data necessary for the performance of their defined tasks. The Data was packed and stored for the convenience of the application that gathered, generated and stored the material.

Data sharing is no longer a specialist technological function to be tackled by system designers and programmers. It has become a business need for growth.

Businesses are dependent on the sharing and distribution of data to support both operational and analytical needs. Information exchange cannot be handled as a courtesy; the processing and data sharing process cannot be viewed as a one-off necessity.

If the Data of an organization is actually a business property, then all data must be bundled and ready for sharing. In order to treat data as an opportunity instead of a strain on enterprise, a strategic plan must approach information provisioning as a standard business procedure.

4. Process

Process is the component of a data strategy that addresses the activities required to evolve data from a raw ingredient into a finished good.

It is normal for companies to set up a hierarchical information purification, standardization, conversion and implementation department for the data warehouse. Sadly, most people have learned that this method of storage is not exclusive to a data warehouse. Many data users need ready-to-use information–so that these customers end up taking part in the development effort themselves. Developing a code for defining and matching documents across these different sources can be quite complicated, particularly when some systems need information from different sources.

While most organizations have programs to tackle data reuse and integration in application development, they have not based their emphasis on providing information that is ready to be used and facilitates recycling and recycle. It is not feasible (or appropriate) for data users to become programmers. Having information available for use is about providing resources and creating mechanisms to produce information which people can use –without the intervention of an organization.

5. Govern

Few companies have fully developed the strategies and processes required for handling information outside the scope of the program and across the enterprise. While many have started engaging in information management programs, others are still in the early stages of their respective initiatives.

When governance knowledge increases and data sharing and use concerns become more apparent, governance programs also expand in reach.

As these programs grow, organizations that create a collection of information policies, rules and methods to ensure consistent usage, use, and management of data.

Efficient data governance ensures that data is managed, manipulated and accessed consistently, whether it is for the determination of security details, data correction logic, data naming standards or even for the establishment of new data rules. Decisions on how information is stored, exploited or distributed are not taken by an independent developer; they are laid down in the rules and policies of data governance.

It should not be surprising that data governance must be included in a data strategy. It is simply impractical to step ahead–without an organized management strategy–in drawing up a plan and a road map to tackle all the aspects in which data is collected, processed, handled and used. Information management provides the necessary rigor over the quality of the information when changes occur in the areas of technology, storage, and practice involved with the information strategy initiative.

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