BUILDING A DATA-CENTRIC STRATEGY AND ROADMAP

Transcription

1 BUILDING A DATA-CENTRIC STRATEGY AND ROADMAP Focus on What Really Matters A Data Blueprint White Paper datablueprint.com W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 1

2 TABLE OF CONTENTS WHY DO YOU NEED A DATA STRATEGY? 3 DATA STRATEGY OVERVIEW 4 DETERMINING THE BUSINESS NEEDS 6 MEASUREMENT & SUCCESS CRITERIA 9 ANALYZING THE CURRENT STATE 12 DEVELOPING A SOLUTION TO ADDRESS NEEDS 14 DEVELOPING A ROADMAP & PLAN 22 RESOURCES 23 ABOUT THE AUTHORS 24 ABOUT DATA BLUEPRINT W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 2

3 WHY DO YOU NEED A DATA STRATEGY? COMPETING IN TODAY S MARKETPLACE REQUIRES DATA-DRIVEN STRATEGIES The future winning companies will compete on being competent, agile and innovative with data and information. We believe that the future marketplace for almost every industry is going to be heavily reliant on information to compete. While everyone may acknowledge this, very few organizations actually live it. Our view is that the future winning companies will compete on being competent, agile and innovative with data and information. In order to succeed in this informationdriven environment, data needs to be at the heart of your organizational business model and strategy. Unfortunately, we have found that there is still a wide gap between how things should be and the reality how they currently are. The majority of companies today are unable to compete effectively with data. Generally, people think of data as having a direct impact on business value by helping them determine which products to sell to which customers and in what context. In the information economy, this has evolved to mean much more: Data is becoming inextricably linked to, and part of, the actual products and services being sold. Clients realize enhanced value in having relevant, accurate and meaningful information as part of their purchases and sellers are willing to pay a premium for this information. Information = Power in the competitive marketplace. Situational awareness, i.e. a 360º view of your customers, suppliers, competitors and operating environment, creates a competitive advantage and enables you to plan and react ahead of time. Volume and velocity of data are impacting operating models. Organizations are being put at greater operating and reputational risk because legacy business processes and systems are straining under the requirements to process and understand ever-increasing volumes and speed of data. These are examples, in our view, of why an organization needs a data strategy. Notice that we purposely do not mention any technical data terms such as Big Data, predictive analytics, improved data quality, data warehousing, data architecture or data governance here as reasons for needing a data strategy. The point is that a data strategy is very foundational. Enterprise data architecture, governance and analytics are not reasons why companies exist. They exist to make a profit. And they make profits by creating value that is uniquely their own; that is, value is created based on their unique ideas, skills, business models, capabilities, organizational structures, brand and culture. To understand how a business uses these things to create value is required to understand how data can be leveraged. Only when there is a clear set of business needs can you begin to develop a data strategy. And that, in our minds, highlights why a data strategy is important W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 3

4 DATA STRATEGY OVERVIEW The Why, What and How of a Data Strategy - with an emphasis on the Why ENTERPRISE DATA STRATEGY CHOICES Data strategy needs to be understood in the context of strategy in general. Michael Porter is one of the leading experts who has done notable work in this space. We utilized his diagram of innovation versus efficiency to visualize enterprise data strategy choices. Data as a business asset creates value across the two dimensions operational efficiencies and business innovation as shown in Figure 1. This includes developing a plan with measurable objectives to get better within their current quadrant (Q) and be more prepared to move to the next quadrant. Organizational Data Management practices mature through a progression from Q1 to Q4. Stage 1: For organizations in Q1, data management is not seen as strategically important to the organization and little is attempted beyond keeping the doors open. Instead, minimal efforts are expended as required to sustain operations (i.e., cash balances instead of cash forecasts). In our experience, this is where the vast majority of companies reside. Fig. 1 Organizational data strategy foci Stage 2: In Q2, organizational needs indicate a data strategy focused on increasing organizational effectiveness and efficiencies. These might be applicable to supporting a lean supply chain management or low cost provider model. Stage 3: Q3 organizations have achieved the ability to use data to invent or dramatically re-imagine various business models. Capital One is repeatedly mentioned as one such organization, innovating around the idea of providing products for underserved credit populations. Stage 4: Q4 organizations have refined and improved upon Q2 and Q3 practices. The final stage should tangibly improve efficiencies for the implementation of sustainable strategies. Organizations should typically progress through all four stages. They correspond to the stages of life when we first learn to crawl, then walk, and run. Most organizations overestimate their data knowledge and attempt to accomplish Q4 data initiatives using a Q1 foundation. The challenge when maturing your organization is that the knowledge, skills, and abilities that can successfully implement operations in Q1 are completely different from the knowledge, skills, and abilities that are required in Q4. While it is possible to jump from Q1 to Q4, any accomplishments will take longer, deliver less, and cost more than a Q4 effort built both on proven Q2 and Q3 foundations. This is not an easy feat and many organizations struggle with it. Only 10% of organizations even think about data strategy W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 4

5 PUTTING THE DATA STRATEGY TOGETHER Step 1: Get on the same page with your business partners You need to get a true understanding of your organization s competitive advantage and current business goals. Work with business leaders, managers, and operators to define specific opportunities that meet your organizational goals. Step 2: Measure business value This is a highly collaborative effort with your business team. In order for a top down strategy to work, you need their buy-in. Collaborate with your business partners and define the metrics that measure your organization s levels of success. Step 3: Develop a holistic solution and approach Develop a comprehensive solution utilizing people, process, data and technology. Outline an achievable implementation plan in a roadmap with timelines, milestones and level of effort estimates. While this 3-step approach may seem simple and straightforward, many organizations struggle with it. This process requires a transformation in how many organizations think and operate this is the greatest challenge in becoming a data-driven organization. A FRAMEWORK FOR DEVELOPING A DATA STRATEGY Following the above approach for putting the data strategy together, we use a framework to create the output that collectively is the data strategy: (1) to define the business needs - the why in the data strategy; (2) to understand the current state the state of the existing assets available; and (3) to outline the strategic solution a comprehensive solution creating measurable business value and sustainable organizational capabilities. The data strategy roadmap outlines an implementation approach, a high-level plan, activities and estimates for LOE and budgets, where possible. The more comprehensive the data strategy, the greater the level of confidence in the roadmap will be. Bus. Strategy & Objectives Competitive Advantage Bus. Structures Bus. Measures Business Need DATA STRATEGY 1 2 Current State Org. Readiness Bus. Processes Bus. & Data Practices Data Assets Tech Assets Value Target 3 Solution Source Capabilities 4 Road Map Figure 2: Data Strategy Framework W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 5

6 Depending on the business needs, this data strategy framework can be applied at multiple levels within the business enterprise, division or functional area. The following sections of this paper will go into the detail of each of the four components depicted above in Figure 2. DETERMINING THE BUSINESS NEEDS This section is about getting on the same page with your business partners: Get a foundational understanding of your business model and look at specific business opportunities UNDERSTANDING YOUR COMPANY S COMPETITIVE ADVANTAGE It is important to have a fundamental understanding of your organization s positioning in the marketplace if you want to truly understand how an organization creates value. Your competitive advantage is the foundation on which your organization has built its success. There are several ways to look at this and numerous business experts have created various frameworks. These are the concepts that management consultants think about. In this section, we encourage you to think like a management consultant. Quite often there is confusion around understanding what competitive advantage truly is. It may seem counter-intuitive, but the competitive advantage is not about being the best Being the best is sometimes referred to as a race to the bottom. Being the best means everybody is doing the exact same thing and the winner will be the one who does it the cheapest. The idea of competitive advantage is to create value in a unique way from other companies in that same market segment. The key is about adding value in a way no on else is - whether that is being good at providing a low cost solution because your company is focused on operational efficiency or providing something that takes specialized skills to provide and is hard to mimic W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 6

7 PORTER S COMPETITIVE STRATEGIC MATRIX One of the approaches that we like to use is Michael Porter s Competitive Strategic Matrix. Competitive advantage is measured on the dimensions of cost and differentiation. Typically your organization cannot inhabit all four quadrants at the same time. The key here is to understand where in these four quadrants your company is uniquely positioned. Questions you want to ask yourself about your organization include: Product Differentiation: Are you focused on differentiation? How specifically focused are your products? Organizations focused on this quadrant keep a focus on innovation. They invest heavily in R&D and constantly introduce new ideas and product features to stay competitive and further evolve their competitive advantage. Examples: Whole Foods, Apple, Gartner Cost: Are you competing on cost? How cost sensitive is your market? Organizations focused on this quadrant tend to prefer low cost with a focus on operational efficiencies and low cost suppliers. Their price is the one thing that sets them apart from their competition. Examples: Walmart, Dollar General, Kia Market Scope: Are you focused on a narrow market, i.e. niche, or a broad customer base? Organizations that pursue narrow market segments provide specialized solutions to niche groups of target buyers whereas organizations with a broad range of buyers offer general value to a wide range of buyers. Examples: Swatch has a niche customer base; Kroger has a broad customer base. Blue Ocean Brands: Are you able to focus across market dimensions? Typically an organization cannot be everything to everyone. The key here is to understand where in these four quadrants your organization is uniquely positioned. Very few, exceptional organizations are positioned exactly in the middle. Examples: Target, Vizio Where are you? Market positioning is no accident. Your business partners are well aware of their market positioning, even if they do not use this same terminology. To them it may be so obvious that they do not realize that others, including IT and data management groups, do not have the same understanding. Take the time to think about where your organization is positioned because that is the first step in understanding how a data strategy can create real value for your organization. A company that is serving a cost-sensitive market will want to focus and develop a different data strategy than a company this is focused on creating differentiated premium products. An organization can t be everything to everyone. The key is to understand where in the four quadrants your organization is uniquely positioned. After discovering where your company is positioned in the market, let s look at the details that make your company unique within that market position W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 7

8 PORTER S FIVE FORCES Once you find your place in the four quadrants what is your competitive advantage? Porter s Five Forces is an analysis construct for understanding specifically how a business uniquely creates value relative to the fundamental market forces that drive value creation. The five forces are depicted in Figure 3. We use Porter s Five Forces to zero in on what is most important to a company and therefore direct the focus on the data assets that are most meaningful. The basic idea is that a company has some degree of advantage with certain Forces and some degree of disadvantage in other Forces. For example, a low cost retail organization may be able to push their suppliers for more favorable terms because they move large volumes of merchandise but, at the same time, the low-cost retailer may be at threat of substitute products because their customers are highly price sensitive. Again, Porter s Five Forces is just one way to analyze a company s competitive advantage and operating model. There are other proven analysis models and each will have its own advantages and disadvantages. The important point is that to develop a truly effective strategy for leveraging data as an asset it requires an explicit understanding of where the company is positioned in the marketplace and how they have been constructed to successfully compete. Bargaining Power of Buyers: The degree of leverage customers have over your company Figure 3: Porter s Five Forces Bargaining Power of Suppliers: The degree of leverage suppliers have over your company Threat of New Entrants: How hard is it for new competition to enter the market? Threat of Substitute Products: How easy (or hard) is it for customers to switch to alternative products? Competitive Rivalry: How competitive is your marketplace? Again, the key takeaway here is that it is important for your organization to have some sort of a framework to help you figure out your competitive advantage because more than likely, your organizational business strategy is either focused on showing up in a strong position or trying to push back when you are at a disadvantage. DATA VALUE GENERATION TAKEAWAY The key here is to determine what is important to your business partners and focus there. When you are faced with an enormous amount of data, this step really helps to simplify it, make it more manageable and focus your attention W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 8

9 SUMMARY: ON THE SAME PAGE WITH YOUR BUSINESS PARTNERS If you want to be effective and ensure long-term success, your data strategy must be business focused! Steps on how to achieve this: 1. Understand the business fundamentals of your organization 2. Develop a common language and shared perspective with your business partners to enable collaboration 3. Identify specific business opportunities or areas of improvement 4. Focus the data strategy solution on improving those specific business needs 5. Measure the business value of making improvements This approach is useful for a couple of reasons. First of all, it sets a vision for WHY we practice data management. Secondly, setting a clear understanding of what is to be achieved for the business makes success much more attainable. This is something that people often struggle with. Amazingly, many people skip this very important step. In our view, one of the reasons why they are skipping it is because people look at data as having IT-driven goals and not business goals. This is why we need to have more business ownership and accountability for how data is leveraged within the organization. TWO DATA STRATEGY SCENARIOS Based on our experience with data strategy, organizations generally find themselves in one of two possible scenarios: 1. The ideal scenario: The organization has a great business strategy, as well as an IT strategy and a data strategy. All three are perfectly aligned and are working together really well. This is how it should be. Unfortunately, we only see this in about 10% of our clients. 2. While the other 90% have some kind of a strategy, it is often not well expressed or needs improvement. In this situation, we recommend a closer look at your organization s competitive advantage and strategic needs in order to add some detail and direction to your ambiguity. You need to have a business strategy that you can use as a base for all your other strategic parts. MEASUREMENT & SUCCESS CRITERIA In Business terms! This often represents a critical, challenging and somewhat elusive activity for many. Ultimately, this is the outcome that matters to the business. MEASURING BUSINESS VALUE In order to fully understand your organization s business goals and objectives, you need to understand how their success will be measured. Success criteria need to be defined along with specific metrics. This activity must be done in collaboration with your business partners. The process of defining metrics and criteria is not always intuitive and can seem difficult at first. It takes a clear business need that can be measured to derive the value of the underlying data strategy activities W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 9

10 From a data management perspective, this is something that we have often struggled with. How do you measure and quantify the value of data quality? How about the value of an enterprise architecture? How do you quantify improving data quality? Does it really add value to have all your data accurate, complete, valid, etc.? It is very hard to quantify or measure the business value of these activities in and of themselves. In the absence of a clear value proposition, the business value of the data strategy is unknown. We have found that it takes a clear business need that can be measured to derive the value of the underlying data strategy activities. In the absence of a clear value proposition, the business value of the data strategy is unknown. It is as if we are only looking at one side of the equation, the cost side (since we cannot measure the value side). This shows why learning to measure the value of your business opportunities is a critical step in the development of a successful data strategy. If something is important to the business, it can be observed. If it can be observed, it is measureable. We use the following three concepts to derive the measures of success. Understanding measurement ; reducing uncertainty, not necessarily an exact value Object of Measurement; often too ambiguously defined Methods of Measurement; become familiar with multiple methods and apply in the right context CLIENT EXAMPLE: MEASURING BUSINESS VALUE This example is based on one of our clients, an international chemical company. They are heavily involved in research and development to create specialized chemicals that are additives for engine oil and gasoline. At more than $1 billion in revenues, they are a fairly large company. If a client requests an oil for high performance cars that will keep the engines running cooler, researchers and scientists at this organization will customize a formulation that meets the product specifications. Over the years, researchers have run tens of thousands of these tests, each ranging from $25,000 to $250,000. CLIENT EXAMPLE: OBJECTS OF MEASUREMENT & METRICS In this case, the object of measurement that we wanted to focus on were three things: 1. Test execution Number of tests per client product formulation Grouped by product types and product complexity 2. Customer satisfaction Amount of time to develop a certified custom formulated product Time from initial request to certification 3. Researcher productivity Tested and certified formulations per researcher With the data of all previous test runs available, our chemical company wanted to leverage that data in such a way that the number of future tests can be reduced because they can make assumptions based on past test results. The idea is to get to the point where they can almost formulate solutions virtually W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 10

11 CLIENT EXAMPLE: OVERVIEW OF EXISTING PROCESS: Our team created a process map of the current state and gained important insights. When we added the dimension of time to the process map, we identified six major categories of non-value added work that the researchers and scientists were engaging in. This represented an important finding because it showed that they are only about 20% productive. The other 80% of their time were taken up by figuring out how to make the data more manageable so that they can begin to analyze it. This is an organization that is very focused on innovation and R&D. However, our analysis revealed that their current R&D process is very inefficient. By optimizing their data management practices and aligning their data strategy with their business strategy and value proposition of being innovative, the entire group can become more productive and more innovative. According to our client s internal business case development, they expect to realize a $25 million gain each year thanks to this data integration. The key here was to put a data strategy in place that included elements of process improvements, architectural components, as well as data quality aspects to provide them with a more integrated system development process. This reduced the number of tests that were needed to find the optimal formulation of chemicals, increased the number of tests available per researcher and reduced the overall time to market for new product development. SUMMARY: MEASURING BUSINESS VALUE The old adage if you don t measure it,it can t be managed is true. The concept of measurement is a skill unto itself and it may be beneficial to add someone to your team who has a background in statistics, operations research or financial analysis. You need to become familiar with these concepts and you need to practice these skills. This is an important step that cannot be skipped. Learning to measure business value requires: Fundamental understanding of what it means to measure Being clear about what is going to be the object of measurement and the specific metrics Applying methods that ensure that the metrics captured are meaningful and consistent The important point here is that you cannot really develop your data strategy and solution that drive business value if you are unsure about how the business measures that value. You will find that if you collaborate closely with your business partners and begin to understand how they think about these things, it will really endear you to them. You will be speaking a common language and you understand how to measure that value so that you can really help them measure their own success. This builds trust that the data strategy is really important to the overall business model. Ultimately, getting this buy-in is exactly what you need W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 11

12 ANALYZING THE CURRENT STATE The solution that gets developed needs to be holistic and therefore the inputs, the base you are starting from, needs to match it. That is, the base is made up of the current assets and capabilities available to be leveraged in any solution that is developed. We propose the following components to be included in a data strategy: People and Organization Business Processes Data Management Practices Data Assets Technology Assets It is important to understand not only how each of these components individually contribute to the solution, but also how they interact as part of a holistic solution. Figure 4 depicts each component and how they interact to form a comprehensive solution. People & Organization Enables Provides Context Delivers Business Processes Enables Business Goals and Objectives Informs Creates Enables Enables Enables Data Assets Technology Assets Data Mgmt. Practices Figure 4: Current State Analysis Framework We are analyzing the current state to understand the following: Identify what can be leveraged in the solution that already exists. Something may exist in one area of the company that can be used in the broader solution. Where are the pain points? What gaps may need to be closed? How does it all hang together across the organization? What are the interdependencies in the operating environment? Most importantly, is the organization capable or willing to make the necessary changes needed? Ensures solutions are achievable PEOPLE AND ORGANIZATION The current state analysis will try to determine the following: The current roles, responsibilities & accountability for all the other components within the analysis framework i.e. business processes, data assets, data management practices and technology assets. What can be achieved with the existing skills and capabilities as well as where can training and W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 12

13 educational have the most benefit? Sometimes the analysis will uncover where existing skills can be combined more effectively to create more capability. How adaptable the organization is to change? That is to understand how well the culture supports adaptability and change in organizational thinking. We also hope to find what can be leveraged to enable a shift towards a data-driven environment. (Note: Culture is typically the biggest impediment to a shift in organizational thinking about data). BUSINESS PROCESSES Business value is realized via a business process. Therefore business processes are at the center of any strategic data solution because it will be through existing or new business processes that value gets created. During the analysis of existing business processes we want to uncover the following: Who are the process actors, including data creators and data consumers What are the pain points in the existing business processes What are the existing performance measures of business processes DATA MANAGEMENT PRACTICES The current state analysis will identify where the data management practices are deficient; where they are deficient surely the data will be as well. While it will be important to look at the actual data (assets), it s the practices around the data that will be most indicative of the current capabilities and gaps to leverage data as an asset. As examples, the following would be data management practices of interest: The process for sharing data across organization. How explicit is that process defined and how formally is it governed. How data quality issues identified and remediated. How data assets are designed and implemented How data assets are controlled, protected and maintained once they are operational e.g. compliance, security, business continuity Data management practices include all business and technology activities related to managing data. Example business activities include business planning, business roles and responsibilities, communications and asset management. Technology activities, where data has traditionally been managed, include system design and development. DATA ASSETS The current state analysis will take a broad look and a narrow view of the data assets. The broad view will consider the overall inventory of data assets available to the organization. This will set the boundaries for what data and information is possible under current conditions. It will also indicate how well the organization understands what data exists there is often a gap when organizations do not realize what data assets they have and the quality of those assets. This broad view of the data assets provides a datacentric business perspective that cannot be seen easily from business processes. A narrower view of the data assets focuses in on the area under consideration as defined by the business needs. The key to this approach is that it keeps from trying to analyze more data than needed to solve the specifically defined business needs. The analysis will look more closely at the critical data elements by reviewing existing data dictionaries and data models as well as using data profiling tools W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 13

14 to get a closer view. Not all the data is equally important. The key is to identify the data that will have the greatest impact and do the narrowed analysis on that data set to get a better understanding of the opportunities and gaps in the current state. TECHNOLOGY ASSETS Similar to the data assets analysis, we want to take a broad view of all technology assets and then focus in on the most important technology assets as it relates to solving the specific business needs. When taking a broad view, the analysis is typically looking for the following: Enterprise and application architecture artifacts Inventory of technology, software, tools and environments Current standards vs. legacy vs. bolt-on technology The narrow view will be looking at the following: Identify specific systems, technology, etc in scope Assess capabilities and constraints Implementation approach e.g. customized or off the shelve The current state analysis should uncover any shadow technology systems. These are critical to find because they are clear indications of gaps in the broad technology solutions. Shadow systems are a wealth of information on what could be achieved with a more strategic approach. HOW TO ANALYZE THE CURRENT STATE Now that we have discussed what each area of the current state analysis is looking for, the following is an outline for how to execute the analysis: Interviews and Surveys Document and artifact reviews Intranet and wiki reviews Facilitated sessions i.e. Workshops Leverage existing organizational structures i.e. working groups, governance teams Requisite Skills: Critical Thinking, Inquisitiveness, Collaboration, Tenacity, Organization and Technical Writing DEVELOPING A SOLUTION TO ADDRESS NEEDS Once we understand the problem and how we are going to measure it, we focus on developing a solution that fits within these parameters. The data strategy solution should be tailored to solve specific business needs and be measurable against set targets. The solution should also enable repeatability in how the business needs are meet. This occurs by developing sustainable organizational capabilities across the same components discussed in the current state analysis i.e. people/organization, business processes, data management practices, data and technology assets W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 14

15 In addition, the solution needs to consider what is possible given the current state capabilities. While the solution may include developing new capabilities, it will not be able to go from crawling to running in the first instance. So the solution needs to be realistic and achievable. The roadmap should reflect this; it should lay out a realistic path of linked activities that delivers value without over-promising. There will be more on this in the final Roadmap section of this paper. The important point to consider about the road map while developing a strategic data solution is that the solution must have enough specificity to lay out an approach and road map. The solution does not need to try to design the actual solution, just give enough scope and definition to what needs to be done but not specifically how. That is what the road map will drive. The biggest challenge, typically, in any data strategy solution is getting the organization to think differently about data. One way to look at this is to consider how the approach to systems and process design changes when you take a data-centric view. THINKING ABOUT DATA DIFFERENTLY Typical Thinking: Application-centric In support of strategy, organizations develop specific goals and objectives These goals and objectives drive the development of specific systems & applications Development of systems & applications leads to network & infrastructure requirements Data & information are typically considered after the systems & applications and network & infrastructure have been articulated At first glance, it makes sense that we develop specific goals and objectives in response to an organizational strategy that will help us determine whether we are on track with our strategy or not. It also makes sense to talk about systems and applications in Step 2, as these will help support the achievement of those goals and objectives, followed by Step 3 Networks and Infrastructure Planning. In this scenario, data and information usually become an afterthought. This approach can create problems. If, for example, we decide in Step 3 that our answer is an ERP, then every discussion in the network & infrastructure step becomes about the data and the network for that ERP. This approach guarantees that your data is going to be foreign to the application instead of following organization-wide requirements. The processes are narrowly formed around those applications and hence, very little data reuse is possible W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 15

16 New Thinking: Data-centric In support of strategy, organizations develop specific goals and objectives These goals and objectives drive the development of specific data and information assets with an eye to organization-wide usage Network and infrastructure components are developed to support organization-wide use of data Development of systems and applications is derived from the data and networks architecture Our proposed shift is very subtle. It is perfectly fine to start with your strategy, goals and objectives. But let s develop organization-wide information requirements in the next step. These are assets that your organization needs to manage and you have put a lot of time and money into getting them to that stage. Once organization-wide requirements are required, then, and only then, do we move on to talk about systems and applications. This is a cleaner and less complicated approach to systems development than shown in our previous example. This approach ensures that data assets are developed from an organization-wide perspective so that the system supports the organization needs instead of the other way around and we can maximize our data and information reuse. If we don t do it this way, the only alternative, the traditional systems life cycle, is guaranteed to produce more small piles of data. And that is exactly what we don t want to have. Again, the solution will follow the same holistic structure as proposed in the current state analysis. As a reminder, Figure 5 is repeated below to depict what is involved in the construction of the data strategy solution. People & Organization Enables Provides Context Delivers Business Processes Enables Business Goals and Objectives Informs Creates Enables Enables Enables Data Assets Technology Assets Data Mgmt. Practices Figure 5: Data Strategy Solution Framework W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 16

17 PEOPLE: WHO IS INVOLVED? Data Strategy is not a one-time event but ongoing and therefore requires focused leadership and governance. Organizational leadership needs to be involved. You need the support of a c-level person who reports to the business lines and talks about how data as an asset should be used to achieve the organizational objectives. In addition to that, we have a number of different stakeholders and a data team. Stakeholders include c-levels, lines of business senior management and team leads. The data team includes architects, modelers, developers, analysts and stewards. It is the data stewards who are going to be charged with implementation of your data strategy in the near future. DATA ASSETS: DETERMINE WHAT IS IMPORTANT Determining which data is important is a vital concept that is not always understood. Not all your data is important. Using your business needs and success criteria will help you identify the scope of data needed as part of your overall data strategy solution. Think about all your data meta-types. The ones that make the most sense to us are transactional data, workflow/event data, master & reference data, reporting & analytical data, and metadata. It always comes back to looking at the data in these buckets whether its architecture, governance, quality controls, etc. We often see a data strategy fully focused on the consumption side of these meta-types, e.g. reporting and analytical data, metadata and maybe some of the master data. We propose that the data strategy needs to consider all meta-types, including transactional and event data. These are the sources for reporting and analytics. Master data impacts transactions and events as much as it does reporting and analytics. The point is that all of the types are interrelated to create a holistic ecosystem and your data strategy needs to support that concept. THE CONCEPT OF ROT We are not sure where this concept originated but ROT stands for data that is redundant, obsolete or trivial. What does this mean? It turns out that when we measure, generally 80% of your data is redundant, obsolete or trivial, which means it just gets in the way of your decision-making process. One of the ways in which a data strategy can help is figure out what data is important, why it is important and what we have to do to make it more useful W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 17

18 DATA MANAGEMENT PRACTICES Now that we have covered the people and data components of a data strategy, let s talk about the actual data management practices. Both technical and foundational practice components are important. In developing a data strategy solution, the important point is to understand which components are needed and to what degree are they needed to support the data strategy. Foundational and technical data management practices are integrated concepts and they need to be thought of that way. As a side note, there are other components (data management practices) that are not covered here, e.g. data security, business continuity etc. These are important components and our view could be expanded to include those, especially if they are important to your business. FOUNDATIONAL PRACTICES Organizations tend to focus too narrowly on only one piece of the data strategy equation. In our experience, there are at least three pieces that you need to focus on. Think of it as a three-legged stool made out of data strategy, data architecture and data governance. As an example, people get fixated on a data warehouse and they think that it is going to solve all their problems. While a data warehouse certainly has the potential to do this, we have seen lots of very well functioning warehouses fail because the data team forgot about the remaining legs of the stool. If you are not incorporating at least three of these practices, you are not taking the necessary big picture approach. FOUNDATIONAL PRACTICE: DATA STRATEGY Your data strategy must align to your organizational business strategy and operating model. We consider a data strategy to be a foundational practice. As we have stressed previously, your data strategy must align to your organizational business strategy and operating model. As the marketplace becomes more data-driven, a datafocused business strategy is an imperative. Recently, many organizations are focusing on developing Big Data strategies but they often don t have a regular or little data strategy in place. That is the wrong way to approach this. You don t want a Big Data strategy if you don t have a data strategy first W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 18

19 FOUNDATIONAL PRACTICE: DATA ARCHITECTURE Data Architecture represents the common vocabulary, expressing integrated requirements ensuring that data assets are stored, arranged, managed and used in support of organizational strategy. Whenever you have trouble explaining what data architecture does for an organization, think of it in terms of developing a common vocabulary and formalized concepts that everyone understands. The purpose of data architecture is to help you get on the same page. In our observation, most organizations have data assets that are not supportive of their strategy, so the question becomes, how can they more effectively use their architectures for the strategy implementation? FOUNDATIONAL PRACTICE: DATA GOVERNANCE Data Governance is the exercise of authority and control over the management of your missioncritical data assets. Data Governance is another key piece. At times, data governance can seem like an added bureaucratic layer with little value-add. Utilize the little g approach and develop governance where it matters most. Focus on organizational roles and responsibilities as well as organizational change management activities. Data governance represents the idea of treating data as an asset. Ten years ago we might have concluded that data governance is covered in administration, but it has been a lot more successful since it was broken out and addressed individually. The key is not to avoid data governance. It s not about saying no to people. Sometimes you have to say not yet. TECHNICAL PRACTICES As introduced previously, there are two components to data management practices, namely foundational and technical practices. This section introduces some of the technical practices. This part is really about building structures and how to go about it in a holistic way. We are talking about thinking like an engineer, using architecture. And if you have done all the other parts of your strategy well, this starts to become self-evident. But if you are struggling to figure out what the technical solution should be, it may be because the business needs are not clear (yet) or their measures are still ambiguous. In that case, take a step back and try to figure it out. This does not mean that there won t be any technical challenges, but if you are faced with a challenge in figuring out what you need to do, then that should be a flag. TECHNICAL PRACTICE: DATA QUALITY Data Quality is driven by fit-forpurpose considerations. The goal is improved directional accuracy. Data Quality is driven by fit-for-purpose considerations. The goal here is improved directional accuracy. When working with clients, we focus on your most important data assets and ensure that our solutions address the root cause of any data quality issues, so that your data is correct when it is created. Experience has shown that organizations can never get in front of their data quality issues if they only use the find-and fix approach. Knowing what data is important is critical to data quality. As mentioned earlier on, organizations are faced with huge volumes of data that is flooding in and it cannot all be corrected and standardized all the W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 19

20 time. This is why you want to put controls and governance in place that focus on your most important data. It is the only way that we have ever seen it be successful with data quality. You are going to be really surprised at how much better your organization runs if there are a few key elements of great data governance and quality controls in place. If you have data quality problems and you figure out what data you want to manage, focus in on the root cause. Find-and-fix may seem like you can make progress in the short term but over the long term it won t be sustainable and you will never be able to get ahead of it. First-time-correct is worth the effort. It may look very daunting in the beginning to fix the root cause but this is the only way to really deal with data quality issues. TECHNICAL PRACTICE: DATA INTEGRATION Data Integration requires a common language and understanding. This is an area where many organizations are challenged. When we talk about data strategy, we don t tend to express explicit integration requirements. Data integration requires a common language and semantic understanding. It needs to support multiple perspectives on the same data. It can be instrumental in creating a broad, 360-degree view that can be utilized to draw insights and analytics. The idea of gaining insight is to gain integration and the more we can focus our integration efforts, the easier it becomes to achieve our data strategies. TECHNICAL PRACTICE: DATA PLATFORMS Data Platforms are about decoupling functionalities using engineering and architectural components and incorporating them into holistic systems thinking. Data Platforms are about decoupling functionalities using engineering and architectural concepts and incorporating them into holistic systems thinking. One common misconception that leads to expensive consequences is that no one data platform can answer all your questions. All your engineered components can only be as strong as their weakest component. One example we like to introduce here to show how this ties into data strategy is about unwinding a mainframe in a high volume transactional environment. This organization s business strategy was that they wanted to efficiently process those transactions in their high transaction volume. The more people had to be involved with transactions, they less efficient they were. The business strategy was for high operational efficiency and that translated into straight through processing (STP) concepts in terms of the overall design and architecture. So the STP of transactions meant to minimize the amount of human intervention that was required to process those transactions. What you are able to do then is you have to enable that transaction, which is really a set of data. You also had to figure out what data components, whether it was exception management or some type of lifecycle management or master data, would enable that STP. TECHNICAL PRACTICE: BUSINESS INTELLIGENCE Business Intelligence is about data consumption. When we say Business Intelligence, we really think of this as reporting and analytics. This is part of the data management practices spectrum that is about data consumption. It is highly dependent on other components, including data quality, metadata, data governance, data integration and data platforms. You could think of it as at the end of the data food chain. It becomes obvious when this part is not well done because you will notice spread-marts everywhere and W. Broad Street, Suite C Glen Allen, Virginia Phone datablueprint.com Page 20