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.
The ever-increasing volume of data challenges us to keep pace in ensuring that we use it to its fullest advantage. Sadly, our approach to new data outlets, data forms and applications is often quite reactive. There is a belief that companies have precious little time to formulate a purposeful plan without affecting business continuity.
Most large organizations now consider that, although they can access data from multiple departments, the lack of logical software alignment (simple data meanings and codes) through information systems makes it difficult or impossible to address cross-functional or cross-divisional questions. This reduces their ability to take advantage of potential opportunities or to respond to business challenges. Strategic Data Management is a technique that can address these issues within the general framework of data technology. Based on the assumption that a relatively stable collection of software organizations is at the core of an organization's information processing needs, the Strategy Data Planning is a formalized, top-down, data-centric strategy method that constructs an organizational structure, its processes, and its underlying details as a framework for defining and integrating an interconnected array of information systems.
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Despite strong theoretical reasons for the importance of the Strategic Data Planning method and its use in multiple organizations, empiric studies have not been able to find clear evidence of its overall success. It raises the question of whether the solution is uniformly acceptable. When success is somewhat troublesome, are there lessons to be learned from real corporate experience? The goal of this paper is to comment on the effects of a variety of case studies of planning activities, to provide insights into the circumstances under which Strategic Data Planning is most successful, and to provide directions for future research.
"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 Strategy 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.
The purpose of this paper was to describe the components of the Data strategy. We agree that this is an important task that helps to ensure consistency with who we are, the principles by which we work, and the methods that we use to meet our strategic objectives. Think of the information strategies as the primary roles you occupy as a data-driven enterprise which will help lead your daily work and keep you centered on your strategic goals. Data planning is a crucial and basic building block for all IT projects, irrespective of whether they are organizational or decision-based in nature.
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
They assume that the software plan should be consistent with and help the competitive factors of the enterprise, including increasing revenue and value; reducing costs and complexity, and maintaining sustainability by risk mitigation and implementation of restrictions. Such guidelines do not apply to the sector. Any project which we invest time on, particularly Data related projects, will follow these key drivers.
What is Strategic Data Management? (SDM)
“Strategic Data Management is a set of frameworks that enable an organization to proactively manage its data asset to help deliver on its business objectives; key to this is the ability to measure the impact of the data initiatives based on both activity and value.”
Figure 1: Data strategy lifecycle
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.
Organizations that have developed and implemented an enterprise Data Strategy have recognized the following benefits:
Figure 2: Benefits of Data Strategy
The Power of Data Strategy
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. As new requirements emerge and holes are apparent, the Component Framework is a framework for defining the changes needed across the various data management and engineering fields of your business. 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 stated by Burbank:
“If you’re just doing Data Management, your databases might be running and they’re optimized, and they’re backed up and you’re doing the day-to-day management.”
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.” Burbank agreed, but added:
"It's the opportunity to take your existing product line and market it better, develop it better, use it to improve customer service or to get a 360-degree understanding of your customer. Data Strategy is driven by your organization's overall Business Strategy and business model."
A Framework for Understanding Data Management vs. Data Strategy Needs
Burbank presented a five-level model designed to help its 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 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.
Figure 3: Five-level Data strategy framework
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:
Figure 4: The five core components of a data strategy.
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.
Figure 5: A data card catalog.
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.
Figure 6: Each system creating its own data copies causes a fourfold increase in storage and processing
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.
Figure 7: Customer details stored and referenced differently in each operational application.
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.
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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.
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.
Figure 8: Each data source contains unique data (colored boxes). Since each application creates its own integration logic, the data values may differ across each application.
Strategic Data Management for DELL Technologies
Throughout today's fast-paced online enterprise age, most companies face challenges throughout aligning their data centers with their business objectives and multi-cloud technology. That multi-cloud penetration ensures that internet networking is now as essential as linking business applications and services. Mergers and acquisitions also left most businesses with too many data centers, complicating their budgets and sustainability plans. Aging services and technology hampered the development of data centers. Insufficient electricity and cooling, as well as network capacity and latency problems, present additional challenges.
The Dell Technologies Data Center Strategies Solution helps launch the data center transition so that you can easily understand value and efficiency gains through a data center strategy that is consistent with your market goals and innovation objectives. Our comprehensive, validated methodology guarantees a strong data center plan for your organization:
Engage Stakeholders. Dell Technologies works with you to identify and engage key IT and business stakeholders to gain their perspective on the strategic and financial objectives and constraints for your data center strategy.
Analyze Data. Dell Technologies assesses your “as-is” current state IT infrastructure, facilities and financials, including a comprehensive analysis of your compute, storage, and network infrastructure.
Evaluate Alternatives. Our experts identify gaps to your desired state and develop alternatives to the current state, including the number, size, type, and purpose of data centers, colocation options, pro-con analysis, potential cost savings, as well as impacts and risks.
Develop Roadmap. Dell Technologies uses the review of alternatives, high-level comparison model and your objectives, including business requirements, planned initiatives, growth, and budget, to develop a "to-be" future state recommendation and roadmap to achieve this recommended state.
Figure 9: Dell Technologies data center strategy approach
A high-level correlation (Figure:10) for a specific client relationship between their present "as-is" data center position and the expected "to-be" potential state. In this case, the company had a broad range of data centers both on-site and on-site as a result of mergers and acquisitions. In addition to apparent cost concerns, this also raised problems for a proper recovery plan. The advice of Dell Technologies Consulting Services has streamlined the climate, which has increased operating costs and explained the method to corporate recovery. Our advisors have offered a range of infrastructure support, financial and recruitment company information to help the decision.
Figure 10: Client TO-BE future state
Summary of Benefits
With a data center approach consistent with the priorities set by the key business partners and supported by a thorough understanding of your current data center resources and an in-depth review of the most realistic solutions, you will have the trust you need to enforce the plan. Investment decisions can be made in the understanding that they will bring you closer to your business objectives and at a consistent and calculated rate that satisfies the budgetary and organizational requirements. You will also create a more efficient and seamless data center climate that can improve restoration procedures and on-going activities.
Partnering with Dell Technologies Consulting Services speeds up the implementation of your data center strategy. The seasoned experts have extensive insight into the main software and network technology of today's multicolor era, so they can educate you on the best ways to incorporate the cost and efficiency benefits of these innovations into your data center plan. You will also benefit from working with seasoned experts who have provided similar data center approaches to customers in a variety of industries and who have a strong understanding of data center management, business cases and the strengths of placement service providers.
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