This blog is the final installment in a series focused on reviewing the individual Components of a Data Strategy. This edition discusses the component Govern and the details associated with supporting a Data Governance initiative as part of an overall Data Strategy.
The definition of Govern is:
“Establishing, communicating and monitoring information practices to ensure effective data sharing, usage, and protection”
As you’re likely aware, Data Governance is about establishing (and following) policies, rules, and all of the associated rigor necessary to ensure that data is usable, sharable, and that all of the associated business and legal details are respected. Data Governance exists because data sharing and usage is necessary for decision making. And, the reason that Data Governance is necessary is because the data is often being used for a purpose outside of why it was collected.
I’ve identified 5 facets about Data Governance to consider when developing your Data Strategy. As a reminder (from the initial Data Strategy Component blog), each facet should be considered individually. And because your Data Strategy goals will focus on future aspirational goals as well as current needs, you’ll likely want to consider different options for each. Each facet can target a small organization’s issues or expand to focus on a large company’s diverse needs.
Information policies are high level information-oriented objectives that your company (or organization, or “governing body”) identify. Information policies act as boundaries or guard rails to guide all of the detailed (and often tactical) rules to identify required and acceptable data-oriented behavior. To offer context, some examples of the information policies that I’ve seen include
- “All customer data will be protected from unauthorized use”.
- “User data access should be limited to ‘systems of record’(when available)”.
- “All data shipped into and out of the company must be processed by the IT Data Onboarding team”.
It’s very common for Data Governance initiatives to begin with focusing on formalizing and communicating a company’s information policies.
Business Data Rules
Rules are specific lower-level details that explain what a data user (or developer) is and isn’t allowed to do. Business data rules (also referred to as “business rules”) can be categorized into one of four types:
- These are the “things” that represent the business details that we measure, track, and analyze. (e.g. a customer, a purchase, a product).
- The details that describe the terms and related details about a business (e.g. The customer purchases a product, Products are sold at a store location).
- These are the details associated with the various items and actions within a company (e.g. The company can only sell a product that is in inventory).
- The distillation or generation of new rules based on other rules. (e.g. Rule: A product can be purchased or returned by a customer. Derivation: A product cannot be returned unless it was purchased from the company).
While the implementation of rules is often the domain of a data administration (or a logical data modeling) team, data governance is often responsible for establishing and managing the process for introducing, communicating, and updating rules.
The term quality is often referred to as “conformance to requirements”. Data Acceptance is a similar concept: the details (or rules) and process applied against data to ensure it is suitable for the use intended. The premise of data acceptance is identifying the minimum details necessary to ensure that data can be used or processed support the associated business activities. Some examples of data acceptance criteria include
- All data values must be non-null.
- All fields within a record must reflect a value within a defined range of values for that field (or business term).
- The product’s price must be a numeric value that is non-zero and non-negative.
- All addresses must be valid mailable addresses.
In order to correct, standardize, or cleanse data, data acceptance for a specific business value (or term) must be identified.
A Data Governance Mechanism is the method (or process) to identify a new rule, process, or detail to support Data Governance. The components of a mechanisms may include the process definition (or flow), the actors, and their decision rights.
This is an area where many Data Governance initiatives fail. While most Governance teams are very good in building new policies, rules, processes, and the associated rigor, they often forget to establish the mechanisms to allow all of the Governance details to be managed, maintained, and updated. This is critically important because as an organization evolves and matures with Data Governance, it may outgrow many of the initial rules and practices. Establishing a set of mechanisms to support modifying and updating existing rules and practices is important to supporting the growth and evolution of a Data Governance environment
The strength and success of Data Governance shouldn’t be measured by the quantity of rules or policies. The success of Data Governance is reflected by the adoption of the rules and processes that are established. Consequently, it’s important for the Data Governance team to continually measure and report adoption levels to ensure the Data Governance details are applied and followed. And where they challenges in adoption, mechanisms exist to allow stakeholders to adjust and update the various aspects of Data Governance to support the needs of the business and the users.
Data Governance will always be a polarizing concept. Whether introduced as part of a development methodology, included within a new data initiative, required to address a business compliance need, or positioned within a Data Strategy, Data Governance is always going to ruffle feathers.
Because folks are busy and they don’t want to be told that they need to have their work reviewed, modified, or approved. Data Governance is an approach (and arguably a method, practice, and process) to ensure that data usage and sharing aligns with policy, business rules, and the law. Data Governance is the “rules of the road” for data.
This blog is 4th in a series focused on reviewing the individual Components of a Data Strategy. This edition discusses the component Assemble and the numerous details involved with sourcing, cleansing, standardizing, preparing, integrating, and moving the data to make it ready to use.
The definition of Assemble is:
“Cleansing, standardizing, combining, and moving data residing in multiple locations and producing a unified view”
In the Data Strategy context, Assemble includes all of the activities required to transform data from its host-oriented application context to one that is “ready to use” and understandable by other systems, applications, and users.
Most data used within our companies is generated from the applications that run the company (point-of-sale, inventory management, HR systems, accounting) . While these applications generate lots of data, their focus is on executing specific business functions; they don’t exist to provide data to other systems. Consequently, the data that is generated is “raw” in form; the data reflects the specific aspects of the application (or system of origin). This often means that the data hasn’t been standardized, cleansed, or even checked for accuracy. Assemble is all of the work necessary to convert data from a “raw” state to one that is ready for business usage.
I’ve identified 5 facets to consider when developing your Data Strategy that are commonly employed to make data “ready to use”. As a reminder (from the initial Data Strategy Component blog), each facet should be considered individually. And because your Data Strategy goals will focus on future aspirational goals as well as current needs, you’ll likely want to consider different options for each. Each facet can target a small organization’s issues or expand to focus on a large company’s diverse needs.
Identification and Matching
Data integration is one of the most prevalent data activities occurring within a company; it’s a basic activity employed by developers and users alike. In order to integrate data from multiple sources, it’s necessary to determine the identification values (or keys) from each source (e.g. the employee id in an employee list, the part number in a parts list). The idea of matching is aligning data from different sources with the same identification values. While numeric values are easy to identify and match (using the “=” operator), character-based values can be more complex (due to spelling irregularities, synonyms, and mistakes).
Even though it’s highly tactical, Identification and matching is important to consider within a Data Strategy to ensure that data integration is processed consistently. And one of the (main) reasons that data variances continue to exist within companies (despite their investments in platforms, tools, and repositories) is because the need for standardized Identification and Matching has not been addressed.
Survivorship is a pretty basic concept: the selection of the values to retain (or survive) from the different sources that are merged. Survivorship rules are often unique for each data integration process and typically determined by the developer. In the context of a data strategy, it’s important to identify the “systems of reference” because the identification of these systems provide clarity to developers and users to understand which data elements to retain when integrating data from multiple systems.
Standardize / Cleanse
The premise of data standardization and cleansing is to identify inaccurate data and correct and reformat the data to match the requirements (or the defined standards) for a specific business element. This is likely the single most beneficial process to improve the business value (and the usability) of data. The most common challenge to data standardization and cleansing is that it can be difficult to define the requirements. The other challenge is that most users aren’t aware that their company’s data isn’t standardized and cleansed as a matter of practice. Even though most companies have multiple tools to cleanup addresses, standardize descriptive details, and check the accuracy of values, the use of these tools is not common.
Wikipedia defines reference data as data that is used to classify or categorize other data. In the context of a data strategy, reference data is important because it ensures the consistency of data usage and meaning across different systems and business areas. Successful reference data means that details are consistently identified, represented, and formatted the same way across all aspects of the company (if the color of a widget is “RED”, then the value is represented as “RED” everywhere – not “R” in product information system, 0xFF0000 in inventory system, and 0xED2939 in product catalog). A Reference Data initiative is often aligned with a company’s data strategy initiative because of its impact to data sharing and reuse.
The idea of movement is to record the different systems that a data element touches as it travels (and is processed) after the data element is created. Movement tracking (or data lineage) is quite important when the validity and accuracy of a particular data value is questioned. And in the current era of heightened consumer data privacy and protection, the need for data lineage and tracking of consumer data within a company is becoming a requirement (and it’s the law in California and the European Union).
The dramatic increase in the quantity and diversity of data sources within most companies over the past few years has challenged even the most technology advanced organizations. It’s not uncommon to find one of the most visible areas of user frustration to be associated with accessing new (or additional) data sources. Much of this frustration occurs because of the challenge in sourcing, integrating, cleansing, and standardizing new data content to be shared with users. As is the case with all of the other components, the details are easy to understand, but complex to implement. A company’s data strategy has to evolve and change when data sharing becomes a production business requirement and users want data that is “ready to use”.
This blog is 3rd in a series focused on reviewing the individual Components of a Data Strategy. This edition discusses storage and the details involved with determining the most effective method for persisting data and ensuring that it can be found, accessed, and used.
The definition of Store is:
“Persisting data in a structure and location that supports access and processing across the user audience”
Information storage is one of the most basic responsibilities of an Information Technology organization – and it’s an activity that nearly every company addresses effectively. On its surface, the idea of storage seems like a pretty simple concept: setup and install servers with sufficient storage (disk, solid state, optical, etc.) to persist and retain information for a defined period of time. And while this description is accurate, it’s incomplete. In the era of exploding data volumes, unstructured content, 3rd party data, and need to share information, the actual media that contains the content is the tip of the iceberg. The challenges with this Data Strategy Component are addressing all of the associated details involved with ensuring the data is accessible and usable.
In most companies, the options of where data is stored is overwhelming. The core application systems use special technology to provide fast, highly reliable, and efficiently positioned data. The analytics world has numerous databases and platforms to support the loading and analyzing of a seemingly endless variety of content that spans the entirety of a company’s digital existence. Most team members’ desktops can expand their storage to handle 4 terabytes of data for less than a $100. And there’s the cloud options that provide a nearly endless set of alternatives for small and large data content and processing needs. Unfortunately, this high degree of flexibility has introduced a whole slew of challenges when it comes to managing storage: finding the data, determining if the data has changed, navigating and accessing the details, and knowing the origin (or lineage).
I’ve identified 5 facets to consider when developing your Data Strategy and analyzing data storage and retention. As a reminder (from the initial Data Strategy Component blog), each facet should be considered individually. And because your Data Strategy goals will focus on future aspirational goals as well as current needs, you’ll likely to want to consider the different options for each. Each facet can target a small organization’s issues or expand to focus on a large company’s diverse needs.
The most basic facet of storing data is to identify the type of content that will be stored: raw application data, rationalized business content, or something in between. It’s fairly common for companies to store the raw data from an application system (frequently in a data lake) as well as the cooked data (in a data warehouse). The concept of “cooked” data refers to data that’s been standardized, cleaned, and stored in a state that’s “ready-to-use”. It’s likely that your company also has numerous backup copies of the various images to support the recovery from a catastrophic situation. The rigor of the content is independent of the platform where the data is stored.
There’s a bunch of work involved with acquiring and gathering data to store it and make it “ready-to-use”. One of the challenges of having a diverse set of data from numerous sources is tracking what you have and knowing where it’s located. Any type of inventory requires that the “stuff” get tracked from the moment of creation. The idea of Onboarding Content is to centrally manage and track all data that is coming into and distributed within your company (in much the same way that a receiving area works within a warehouse). The core benefit of establishing Onboarding as a single point of data reception (or gathering) is that it ensures that there’s a single place to record (and track) all acquired data. The secondary set of benefits are significant: it prevents unnecessary duplicate acquisition, provides a starting point for cataloging, and allows for the checking and acceptance of any purchased content (which is always an issue).
Navigation / Access
All too often, business people know the data want and may even know where the data is located; unfortunately, the problem is that they don’t know how to navigate and access the data where it’s stored (or created). To be fair, most operational application systems were never designed for data sharing; they were configured to process data and support a specific set of business functions. Consequently, accessing the data requires a significant level of system knowledge to navigate the associated repository to retrieve the data. In developing a Data Strategy, it’s important to identify the skills, tools, and knowledge required for a user to access the data they require. Will you require someone to have application interface and programming skills? SQL skills and relational database knowledge? Or, spreadsheet skills to access a flat file, or some other variation?
Change control is a very simple concept: plan and schedule maintenance activities, identify outages, and communicate those details to everyone. This is something that most technologists understand. In fact, most Information Technology organizations do a great job of production change control for their application environments. Unfortunately, few if any organizations have implemented data change control. The concept for data is just as simple: plan and schedule maintenance activities, identify outages (data corruption, load problems, etc.), and communicate those details to everyone. If you’re going to focus any energy on a data strategy, data change control should be considered in the top 5 items to be included as a goal and objective.
As I’ve already mentioned, most companies have lots of different options for housing data. Unfortunately, the criteria for determining the actual resting place for data often comes down to convenience and availability. While many companies have architecture standards and recommendations for where applications and data are positioned, all too often the selection is based on either programmer convenience or resource availability. The point of this area isn’t to argue what the selection criteria are, but to identify them based on core strategic (and business operation) priorities.
In your Data Strategy effort, you may find the need to include other facets in your analysis. Some of the additional details that I’ve used in the past include metadata, security, retention, lineage, and archive access. While simple in concept, this particular component continues to evolve and expand as the need for data access and sharing grows within the business world.
This blog is the 2nd in a series focused on reviewing the individual Components of a Data Strategy. This edition discusses the concept of data provisioning and the various details of making data sharable.
The definition of Provision is:
“Supplying data in a sharable form while respecting all rules and access guidelines”
One of the biggest frustrations that I have in the world of data is that few organizations have established data sharing as a responsibility. Even fewer have setup the data to be ready to share and use by others. It’s not uncommon for a database programmer or report developer to have to retrieve data from a dozen different systems to obtain the data they need. And, the data arrives in different formats and files that change regularly. This lack of consistency generates large ongoing maintenance costs and requires an inordinate amount of developer time to re-transform, prepare, fix data to be used (numerous studies have found that ongoing source data maintenance can take as much of 50% of the database developers time after the initial programming effort is completed).
Should a user have to know the details (or idiosyncrasies) of the application system that created the data to use the data? (That’s like expecting someone to understand the farming of tomatoes and manufacturing process of ketchup in order to be able to put ketchup on their hamburger). The idea of Provision is to establish the necessary rigor to simplify the sharing of data.
I’ve identified 5 of the most common facets of data sharing in the illustration above – there are others. As a reminder (from last week’s blog), each facet should be considered individually. And because your Data Strategy goals will focus on future aspirational goals as well as current needs, you’ll likely to want to review the different options for each facet. Each facet can target a small organization’s issues or expand to address a diverse enterprise’s needs.
This is the most obvious aspect of provisioning: structuring and formatting the data in a clear and understandable manner to the data consumer. All too often data is packaged at the convenience of the developer instead of the convenience of the user. So, instead of sharing data as a backup file generated by an application utility in a proprietary (or binary) format, the data should be formatted so every field is labeled and formatted (text, XML) for a non-technical user to access using easily available tools. The data should also be accompanied with metadata to simplify access.
This facet works with Packaging and addresses the details associated with the data container. Data can be shared via a file, a database table, an API, or one of several other methods. While sharing data in a programmer generated file is better than nothing, a more effective approach would be to deliver data in a well-known file format (such as Excel) or within a table contained in an easily accessible database (e.g. data lake or data warehouse).
Source data stewardship is critical in the sharing of data. In this context, a Source Data Steward is someone that is responsible for supporting and maintaining the shared data content (there several different types of data stewards). In some companies, there’s a data steward responsible for the data originating from an individual source system. Some companies (focused on sharing enterprise-level content) have positioned data stewards to support individual subject areas. Regardless of the model used, the data steward tracks and communicates source data changes, monitors and maintains the shared content, and addresses support needs. This particular role is vital if your organization is undertaking any sort of data self-service initiative.
This item addresses the issues that are common in the world of electronic data sharing: inconsistency, change, and error. Acceptance checking is a quality control process that reviews the data prior to distribution to confirm that it matches a set of criteria to ensure that all downstream users receive content as they expect. This item is likely the easiest of all details to implement given the power of existing data quality and data profiling tools. Unfortunately, it rarely receives attention because of most organization’s limited experience with data quality technology.
In order to succeed in any sort of data sharing initiative, whether in supporting other developers or an enterprise data self-service initiative, it’s important to identify the audience that will be supported. This is often the facet to consider first, and it’s valuable to align the audience with the timeframe of data sharing support. It’s fairly common to focus on delivering data sharing for developers support first followed by technical users and then the large audience of business users.
In the era of “data is a business asset” , data sharing isn’t a courtesy, it’s an obligation. Data sharing shouldn’t occur at the convenience of the data producer, it should be packaged and made available for the ease of the user.
Because the idea of building a data strategy is a fairly new concept in the world of business and information technology (IT), there’s a fair amount of discussion about the pieces and parts that comprise a Data Strategy. Most IT organizations have invested heavily in developing plans to address platforms, tools, and even storage. Those IT plans are critical in managing systems and capturing and retaining content generated by a company’s production applications. Unfortunately, those details don’t typically address all of the data activities that occur after an application has created and processed data from the initial business process. The reasons that folks take on the task of developing a Data Strategy is because of the challenges in finding, identifying, sharing, and using data. In any company, there are numerous roles and activities involved in delivering data to support business processing and analysis. A successful Data Strategy must support the breadth of activities necessary to ensure that data is “ready to use”.
There are five core components in a data strategy that work together as building blocks to address the various details necessary to comprehensively support the management and usage of data.
Identify The ability to identify data and understand its meaning regardless of its structure, origin, or location.
This concept is pretty obvious, but it’s likely one of the biggest obstacles in data usage and sharing. All too often, companies have multiple and different terms for specific business details (customer: account, client, patron; income: earnings, margin, profit). In order to analyze, report, or use data, people need to understand what it’s called and how to identify it. Another aspect of Identify is establishing the representation of the data’s value (Are the company’s geographic locations represented by name, number, or an abbreviation?) A successful Data Strategy would identify the gaps and needs in this area and identify the necessary activities and artifacts required to standardize data identification and representation.
Provision Enabling data to be packaged and made available while respecting all rules and access guidelines.
Data is often shared or made available to others at the convenience of the source system’s developers. The data is often accessible via database queries or as a series of files. There’s rarely any uniformity across systems or subject areas, and usage requires programming level skills to analyze and inventory the contents of the various tables or files. Unfortunately, the typical business person requiring data is unlikely to possess sophisticated programming and data manipulation skills. They don’t want raw data (that reflects source system formats and inaccuracies), they want data that is uniformly formatted and documented that is ready to be added to their analysis activities.
The idea of Provision is to package and provide data that is “ready to use”. A successful Data Strategy would identify the various data sharing needs and identify the necessary methods, practices, and tooling required to standardize data packaging and sharing.
Store Persisting data in a structure and location that supports access and processing across the enterprise.
Most IT organizations have solid plans for addressing this area of a Data Strategy. It’s fairly common for most companies to have a well-defined set of methods to determine the platform where online data is stored and processed, how data is archived for disaster recovery, and all of the other details such as protection, retention, and monitoring.
As the technology world has evolved, there are other facets of this area that require attention. The considerations include managing data distributed across multiple locations (the cloud, premise systems, and even multiple desktops), privacy and protection, and managing the proliferation of copies. With the emergence of new consumer privacy laws, it’s risky to store multiple copies of data, and it’s become necessary to track all existing copies of content. A successful Data Strategy ensures that any created data is always available for future access without requiring everyone to create their own copy.
Assemble Standardizing, combining, and moving data residing in multiple locations and providing a unified view.
It’s no secret that data integration is one of the more costly activities occurring within an IT organization; nearly 40% of the cost of new development is consumed by data integration activities. And Assemble isn’t limited to integration, it also includes correcting, standardizing, and formatting the content to make it “ready to use”.
With the growth of analytics and desktop decisioning making, the need to continually analyze and include new data sets into the decision-making process has exploded. Processing (or preparing or wrangling) data is no longer confined to the domain of the IT organization, it has become an end user activity. A successful Data Strategy had to ensure that all users can be self-sufficient in their abilities to process data.
Govern Establishing and communicating information rules, policies, and mechanisms to ensure effective data usage.
While most organizations are quick to identify their data as a core business asset, few have put the necessary rigor in place to effectively manage data. Data Governance is about establishing rules, policies, and decision mechanisms to allow individuals to share and use data in a manner that respects the various (legal and usage) guidelines associated with that data. The inevitable challenge with Data Governance is adoption by the entire data supply chain – from application developers to report developers to end users. Data Governance isn’t a user-oriented concept, it’s a data-oriented concept. A successful Data Strategy identifies the rigor necessary to ensure a core business asset is managed and used correctly.
The 5 Components of a Data Strategy is a framework to ensure that all of a company’s data usage details are captured and organized and that nothing is unknowingly overlooked. A successful Data Strategy isn’t about identifying every potential activity across the 5 different components. It’s about making sure that all of the identified solutions to the problems in accessing, sharing, and using data are reviewed and addressed in a thorough manner.
A simple definition of Data Strategy is
“ A plan designed to improve all of the ways you acquire, store, manage, share, and use data”
Over the years, most companies have spent a fortune on their data. They have a bunch of folks that comprise their “center of expertise”, they’ve invested lots of money in various data management tools (ETL-extract/transformation/load, metadata, data catalogs, data quality, etc.), and they’ve spent bazillions on storage and server systems to retain their terabytes or petabytes of data. And what you often find is a lot of disparate (or independent) projects building specific deliverables for individual groups of users. What you rarely find is a plan that addresses all of the disparate user needs that to support their ongoing access, sharing, use of data.
While most companies have solid platform strategies, storage strategies, tool strategies, and even development strategies, few companies have a data strategy. The company has technology standards to ensure that every project uses a specific brand of server, a specific set of application development tools, a well-defined development method, and specific deliverables (requirements, code, test plan, etc.) You rarely find data standards: naming conventions and value standards, data hygiene and correction, source documentation and attribute definitions, or even data sharing and packaging conventions. The benefit of a Data Strategy is that data development becomes reusable, repeatable, more reliable, faster. Without a data strategy, the data activities within every project are always invented from scratch. Developers continually search and analyze data sources, create new transformation and cleansing code, and retest the same data, again, and again, and again.
The value of a Data Strategy is that it provides a roadmap of tasks and activities to make data easier to access, share, and use. A Data Strategy identifies the problems and challenges across multiple projects, multiple teams, and multiple business functions. A Data Strategy identifies the different data needs across different projects, teams, and business functions. A Data Strategy identifies the various activities and tasks that will deliver artifacts and methods that will benefit multiple projects, teams and business functions. A Data Strategy delivers a plan and roadmap of deliverables that ensures that data across different projects, multiple teams, and business functions are reusable, repeatable, more reliable, and delivered faster.
A Data Strategy is a common thread across both disparate and related company projects to ensure that data is managed like a business asset, not an application byproduct. It ensures that data is usable and reusable across a company. A Data Strategy is a plan and road map for ensuring that data is simple to acquire, store, manage, share, and use.
In order to prepare for the cooking gauntlet that often occurs with the end of year holiday season, I decided to purchase a new rotisserie oven. The folks at Acme Rotisserie include a large amount of documentation with their rotisserie. I reviewed the entire pile and was a bit surprised by the warranty registration card. The initial few questions made sense: serial number, place of purchase, date of purchase, my home address. The other questions struck me as a bit too inquisitive: number of household occupants, household income, own/rent my residence, marital status, and education level. Obviously, this card was a Trojan horse of sorts; provide registration details –and all kinds of other personal information. They wanted me to give away my personal information so they could analyze it, sell it, and make money off of it.
Companies collecting and analyzing consumer data isn’t anything new –it’s been going on for decades. In fact, there are laws in place to protect consumer’s data in quite a few industries (healthcare, telecommunications, and financial services). Most of the laws focus on protecting the information that companies collect based on their relationship with you. It’s not the just details that you provide to them directly; it’s the information that they gather about how you behave and what you purchase. Most folks believe behavioral information is more valuable than the personal descriptive information you provide. The reason is simple: you can offer creative (and highly inaccurate) details about your income, your education level, and the car you drive. You can’t really lie about your behavior.
I’m a big fan of sharing my information if it can save me time, save me money, or generate some sort of benefit. I’m willing to share my waist size, shirt size, and color preferences with my personal shopper because I know they’ll contact me when suits or other clothing that I like is available at a good price. I’m fine with a grocer tracking my purchases because they’ll offer me personalized coupons for those products. I’m not okay with the grocer selling that information to my health insurer. Providing my information to a company to enhance our relationship is fine; providing my information to a company so they can share, sell, or otherwise unilaterally benefit from it is not fine. My data is proprietary and my intellectual property.
Clearly companies view consumer data to be a highly valuable asset. Unfortunately, we’ve created a situation where there’s little or no cost to retain, use, or abuse that information. As abuse and problems have occurred within certain industries (financial services, healthcare, and others), we’ve created legislation to force companies to responsibly invest in the management and protection of that information. They have to contact you to let you know they have your information and allow you to update communications and marketing options. It’s too bad that every company with your personal information isn’t required to behave in the same way. If data is so valuable that a company retains it, requiring some level of maintenance (and responsibility) shouldn’t be a big deal.
It’s really too bad that companies with copies of my personal information aren’t required to contact me to update and confirm the accuracy of all of my personal details. That would ensure that all of the specialized big data analytics that are being used to improve my purchase experiences were accurate. If I knew who had my data, I could make sure that my preferences were up to date and that the data was actually accurate.
It’s unfortunate that Acme Rotisserie isn’t required to contact me to confirm that I have 14 children, an advanced degree in swimming pool construction, and that I have Red Ferrari in my garage. It will certainly be interesting to see the personalized offers I receive for the upcoming Christmas shopping season.
It’s fairly common for companies to assign Executive Sponsors to their large projects. “Large” typically reflects budget size, the inclusion of cross-functional teams, business impact, and complexity. The Executive Sponsor isn’t the person running and directing the project on a day-to-day basis; they’re providing oversight and direction. He monitors project progress and ensures that tactics are carried out to support the project’s goals and objectives. He has the credibility (and authority) to ensure that the appropriate level of attention and resources are available to the project throughout its entire life.
While there’s nearly universal agreement on the importance of an Executive Sponsor, there seems to be limited discussion about the specifics of the role. Most remarks seem to dwell on the importance on breaking down barriers, dealing with roadblocks, and effectively reacting to project obstacles. While these details make for good PowerPoint presentations, project success really requires the sponsor to exhibit a combination of skills beyond negotiation and problem resolution to ensure project success. Here’s my take on some of the key responsibilities of an Executive Sponsor.
Inspire the Stakeholder Audience
Most executives are exceptional managers that understand the importance of dates and budgets and are successful at leading their staff members towards a common goal. Because project sponsors don’t typically have direct management authority over the project team, the methods for leadership are different. The sponsor has to communicate, captivate, and engage with the team members throughout all phases of the project. And it’s important to remember that the stakeholders aren’t just the individual developers, but the users and their management. In a world where individuals have to juggle multiple priorities and projects, one sure-fire way to maintain enthusiasm (and participation) is to maintain a high-level of sponsor engagement.
Understand the Project’s Benefits
Because of the compartmentalized structure of most organizations, many executives aren’t aware of the details of their peer organizations. Enterprise-level projects enlist an Executive Sponsor to ensure that the project respects (and delivers) benefits to all stakeholders. It’s fairly common that any significantly sized project will undergo scope change (due to budget challenges, business risks, or execution problems). Any change will likely affect the project’s deliverables as well as the perceived benefits to the different stakeholders. Detailed knowledge of project benefits is crucial to ensure that any change doesn’t adversely affect the benefits required by the stakeholders.
Know the Project’s Details
Most executives focus on the high-level details of their organization’s projects and delegate the specifics to the individual project manager. When projects cross organizational boundaries, the executive’s tactics have to change because of the organizational breadth of the stakeholder community. Executive level discussions will likely cover a variety of issues (both high-level and detailed). It’s important for the Executive Sponsor to be able to discuss the brass tacks with other executives; the lack of this knowledge undermines the sponsor’s credibility and project’s ability to succeed.
Hold All Stakeholders Accountable
While most projects begin with everyone aligned towards a common goal and set of tactics, it’s not uncommon for changes to occur. Most problems occur when one or more stakeholders have to adjust their activities because of an external force (new priorities, resource contention, etc.). What’s critical is that all stakeholders participate in resolving the issue; the project team will either succeed together or fail together. The sponsor won’t solve the problem; they will facilitate the process and hold everyone accountable.
Stay Involved, Long Term
The role of the sponsor isn’t limited to supporting the early stages of a project (funding, development, and deployment); it continues throughout the life of the project. Because most applications have a lifespan of no less than 7 years, business changes will drive new business requirements that will drive new development. The sponsor’s role doesn’t diminish with time – it typically expands.
The overall responsibility set of an Executive Sponsor will likely vary across projects. The differences in project scope, company culture, business process, and staff resources across individual projects inevitably affect the role of the Executive Sponsor. What’s important is that the Executive Sponsor provides both strategic and tactical support to ensure a project is successful. An Executive Sponsor is more than the project’s spokesperson; they’re the project CEO that has equity in the project’s outcome and a legitimate responsibility for seeing the project through to success.
Photo “American Alligator Crossing the Road at Canaveral National Seashore”courtesy of Photomatt28 (Matthew Paulson) via Flickr (Creative Commons license).
Companies spend a small fortune continually investing and reinvesting in making their business analysts self-sufficient with the latest and greatest analytical tools. Most companies have multiple project teams focused on delivering tools to simplify and improve business decision making. There are likely several standard tools deployed to support the various data analysis functions required across the enterprise: canned/batch reports, desktop ad hoc data analysis, and advanced analytics. There’s never a shortage of new and improved tools that guarantee simplified data exploration, quick response time, and greater data visualization options, Projects inevitably include the creation of dozens of prebuilt screens along with a training workshop to ensure that the users understand all of the new whiz bang features associated with the latest analytic tool incarnation. Unfortunately, the biggest challenge within any project isn’t getting users to master the various analytical functions; it’s ensuring the users understand the underlying data they’re analyzing.
If you take a look at the most prevalent issue with the adoption of a new business analysis tool is the users’ knowledge of the underlying data. This issue becomes visible with a number of common problems: the misuse of report data, the misunderstanding of business terminology, and/or the exaggeration of inaccurate data. Once the credibility or usability of the data comes under scrutiny, the project typically goes into “red alert” and requires immediate attention. If ignored, the business tool quickly becomes shelfware because no one is willing to take a chance on making business decisions based on risky information.
All too often the focus on end user training is tool training, not data training. What typically happens is that an analyst is introduced to the company’s standard analytics tool through a “drink from a fire hose” training workshop. All of the examples use generic sales or HR data to illustrate the tool’s strengths in folding, spindling, and manipulating the data. And this is where the problem begins: the vendor’s workshop data is perfect. There’s no missing or inaccurate data and all of the data is clearly labeled and defined; classes run smoothly, but it just isn’t reality Somehow the person with no hands-on data experience is supposed to figure out how to use their own (imperfect) data. It’s like someone taking their first ski lesson on a cleanly groomed beginner hill and then taking them up to the top of an a black diamond (advanced) run with step hills and moguls. The person works hard but isn’t equipped to deal with the challenges of the real world. So, they give up on the tool and tell others that the solution isn’t usable.
All of the advanced tools and manipulation capabilities don’t do any good if the users don’t understand the data. There are lots of approaches to educating users on data. Some prefer to take a bottom-up approach (reviewing individual table and column names, meanings, and values) while others want to take a top-down approach (reviewing subject area details, the associated reports, and then getting into the data details). There are certainly benefits of one approach over the other (depending on your audience); however, it’s important not to lose sight of the ultimate goal: giving the users the fundamental data knowledge they need to make decisions. The fundamentals that most users need to understand their data include a review of
- the business subject area associated with their dat
- business terms, definitions, and their associated data attributes
- data values and their representations
- business rules and calculations associated with the individual values
- the data’s origin (a summary of the business processes and source system)
The above details may seem a bit overwhelming if you consider that most companies have mature reporting environments and multi-terabyte data warehouses. However, we’re not talking about training someone to be an expert on 1000 data attributes contained within your data warehouse; we’re talking about ensuring someone’s ability to use an initial set of reports or a new tool without requiring 1-on-1 training. It’s important to realize that the folks with the greatest need for support and data knowledge are the newbies, not the experienced folks.
There are lots of options for imparting data knowledge to business users: a hands-on data workshop, a set of screen videos showing data usage examples, or a simple set of web pages containing definitions, textual descriptions, and screen shots. Don’t get wrapped up in the complexities of creating the perfect solution – keep it simple. I worked with a client that deployed their information using a set of pages constructed with PowerPoint that folks could reference in a the company’s intranet. If your users have nothing – don’t’ worry about the perfect solution – give them something to start with that’s easy to use.
Remember that the goal is to build users’ data knowledge that is sufficient to get them to adopt and use the company’s analysis tools. We’re not attempting to convert everyone into data scientists; we just want them to use the tools without requiring 1-on-1 training to explain every report or data element.
Photo courtesy of NASA. Nasa Ames Research Center engineer H Julian “Harvey” Allen illustrating data knowledge (relating to capsule design for the Mercury program)