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Data Strategy Component: Assemble

assemble

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

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.

Reference Data  

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.

Movement Tracking

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”.

Data Strategy Component: Provision

Provision

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. 

Packaging

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.

Platform 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).

Stewardship

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.

Acceptance Checking

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.

Data Audience

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.

The 5 Components of a Data Strategy

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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.

Do You Need A Data Strategy?

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During my time teaching Data Strategy in the class room, I’m frequently asked the question, “how do I know if I need a data strategy?”  For those of you that are deep thinkers, business strategists, or even data architects, I suspect your answer is either “yes!” or “why not?”.

When I’m asked that question, I actually think there’s a different question at hand, “Should I invest the time in developing a data strategy instead of something else?”

In today’s business world, there’s not a shortage of “to do list” items.  So, prioritizing the development of a Data Strategy means deprioritizing some other item.   In order to understand the relative priority and benefit of a Data Strategy initiative, take a look at the need, pain, or problem you’re addressing along with the quantity of people affected.  Your focus should be understanding how a Data Strategy initiative will benefit the team members’ ability to do their job.

To get started, I usually spend time up front interviewing folks to understand the strengths, weaknesses, challenges, and opportunities that exist with data within a company (or organization).  Let me share 5 questions that I always ask.

  1. Is the number of users (or organizations) building queries/reports to analyze data growing?
  2. Are there multiple reports containing conflicting information?
  3. Can a new staff member find and use data on their own, or does it require weeks or months of staff mentoring?
  4. Is data systematically inspected for accuracy (and corrected)? Is anyone responsible for fixing “broken data”?
  5. Is anyone responsible for data sharing?

While you might think these questions are a bit esoteric, each one has a specific purpose.  I’m a big fan of positioning any new strategy initiative to clearly identify the problems that are going to be solved.  If you’re going to undertake the development of a Data Strategy, you want to make certain that you will improve staff members’ ability to make decisions and be more effective at their jobs.  These questions will help you identify where people struggle getting the job done, or where there’s an unquantified risk with using data to make decisions.

So, let me offer an explanation of each question.

  1. “Is the number of users (or organizations) building queries/reports to analyze data growing”

The value of a strategy is directly proportional to the number of people that are going to be affected. In the instance of a data strategy, it’s valuable to understand the number of people that use data (hands-on) to make decisions or do their jobs. If the number is small or decreasing, a strategy initiative may not be worth the investment in time and effort.  The larger the number, the greater the impact to the effectiveness (and productivity) to the various staff members.

  1. “Are there multiple reports containing conflicting information? “

If you have conflicting details within your company that means decisions are made with inaccurate data.  That also means that there’s mistrust of information and team members are spending time confirming details.  That’s business risk and a tremendous waste of time.

  1. “Can a new staff member find and use data…”

If a new staff member can’t be self-sufficient after a week or two on the job (when it comes to data access and usage), you have a problem.  That’s like someone joining the company and not having access to office supplies, a parking space, and email.  And, if the only way to learn is to beg for time for other team members – your spending time with two people not doing their job. It’s a problem that’s being ignored.

  1. “Is data systematically inspected for accuracy (and corrected)? …”

This item is screaming for attention.  If you’re in a company that uses data to make decisions, and no one is responsible for inspecting the content, you have a problem.  Think about this issue another way:   would you purchase hamburger at the grocery store if there was a sign that stated “Never inspected.  May be spoiled.  Not our responsibility”?

  1. Is anyone responsible for data sharing?

This item gets little attention in most companies and is likely the most important of all the questions.  If data is a necessary ingredient in decision making and there isn’t anyone actively responsible for ensuring that new data assets are captured, stored, tracked, managed, and shared, you’re saying that data isn’t a business asset.  (How many assets in the company aren’t tied to someone’s responsibilities?)

If the answer to all of the questions is “no” – great.  You’re in an environment where data is likely managed in a manner that supports a multitude of team members’ needs across different organizations.  If you answered “yes” to a single question, it’s likely that an incremental investment in a tactical data management effort would be helpful.  If more than 1 question is answered “yes”, your company (and the team) will benefit from a Data Strategy initiative.

 

What is a Data Strategy?

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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.

The Power of Data Virtualization

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I was participating in a discussion about Data Virtualization (DV) the other day and was intrigued with the different views that everyone had about a technology that’s been around for more than 10 years. For those of you that don’t participate in IT-centric, geekfest discussions on a regular basis, Data Virtualization software is middleware that allows various disparate data sources to look like a single relational database.  Some folks characterize Data Virtualization as a software abstraction layer that removes the storage location and format complexities associated with manipulating data. The bottom line is that Data Virtualization software can make a BI (or any SQL) tool see data as though it’s contained within a single database even though it may be spread across multiple databases, XML files, and even Hadoop systems.

What intrigued me about the conversation is that most of the folks had been introduced to Data Virtualization not as an infrastructure tool that simplifies specific disparate data problems, but as the secret sauce or silver bullet for a specific application. They had all inherited an application that had been built outside of IT to address a business problem that required data to be integrated from a multitude of sources.  And in each instance, the applications were able to capitalize on Data Virtualization as a more cost effective solution for integrating detailed data. Instead of building a new platform to store and process another copy of the data, they used Data Virtualization software to query and integrate data from the individual sources systems. And each “solution” utilized a different combination of functions and capabilities.

As with any technology discussion, there’s always someone that believes that their favorite technology is the best thing since sliced bread – and they want to apply their solution to every problem.  Data Virtualization is an incredibly powerful technology with a broad array of functions that enable multi-source query processing. Given the relative obscurity of this data management technology, I thought I’d review some of the more basic capabilities supported by this technology.

Multi-Source Query Processing.  This is often referred to as Query Federation. The ability to have a single query process data across multiple data stores.

Simplify Data Access and Navigation.  Exposes data as single (virtual) data source from numerous component sources. The DV system handles the various network, SQL dialect, and/or data conversion issues.

Integrate Data “On the Fly”.  This is referred to as Data Federation. The DV server retrieves and integrates source data to support each individual query. 

Access to Non-Relational Data. The DV server is able to portray non-relational data (e.g. XML data, flat files, Hadoop, etc.) as structured, relational tables.  

Standardize and Transform Data. Once the data is retrieved from the origin, the DV server will convert the data (if necessary) into a format to support matching and integration.

Integrate Relational and Non-Relational Data. Because DV can make any data source (well, almost any) look like a relational table, this capability is implicit. Keep in mind that the data (or a subset of it) must have some sort of implicit structure.  

Expose a Data Services Interface. Exposing a web service that is attached to a predefined query that can be processed by the DV server.

Govern Ad Hoc Queries. The DV Server can monitor query submissions, run time, and even complexity – and terminate or prevent processing under specific rule-based situations.

Improve Data Security.  As a common point of access, the DV Server can support another level of data access security to address the likely inconsistencies that exist across multiple data store environments.

As many folks have learned, Data Virtualization is not a substitute for a data warehouse or a data mart.  In order for a DV Server to process data, the data must be retrieved from the origin; consequently, running a query that joins tables spread across multiple systems containing millions of records isn’t practical.  An Ethernet network is no substitute for the high speed interconnect linking a computer’s processor and memory to online storage. However, when the data is spread across multiple systems and there’s no other query alternative, Data Virtualization is certainly worth investigating.

Project Success = Data Usability

One of the challenges in delivering successful data-centric projects (e.g. analytics, BI, or reporting) is realizing that the definition of project success differs from traditional IT application projects.  Success for a traditional application (or operational) project is often described in terms of transaction volumes, functional capabilities, processing conformance, and response time; data project success is often described in terms of business process analysis, decision enablement, or business situation measurement.  To a business user, the success of a data-centric project is simple: data usability.

It seems that most folks respond to data usability issues by gravitating towards a discussion about data accuracy or data quality; I actually think the more appropriate discussion is data knowledge.  I don’t think anyone would argue that to make data-enabled decisions, you need to have knowledge about the underlying data.  The challenge is understanding what level of knowledge is necessary.  If you ask a BI or Data Warehouse person, their answer almost always includes metadata, data lineage, and a data dictionary.  If you ask a data mining person, they often just want specific attributes and their descriptions — they don’t care about anything else.  All of these folks have different views of data usability and varying levels (and needs) for data knowledge.

One way to improve data usability is to target and differentiate the user audience based on their data knowledge needs.  There are certainly lots of different approaches to categorizing users; in fact, every analyst firm and vendor has their own model to describe different audience segments.  One of the problems with these types of models is that they tend to focus heavily on the tools or analytical methods (canned reports, drill down, etc.) and ignore the details of data content and complexity. The knowledge required to manipulate a single subject area (revenue or customer or usage) is significantly less than the skills required to manipulate data across 3 subject areas (revenue, customer, and usage).  And what exacerbates data knowledge growth is the inevitable plethora of value gaps, inaccuracies, and inconsistencies associated with the data. Data knowledge isn’t just limited to understanding the data; it includes understanding how to work around all of the imperfections.

Here’s a model that categories and describes business users based on their views of data usability and their data knowledge needs

Level 1: “Can you explain these numbers to me?”

This person is the casual data user. They have access to a zillion reports that have been identified by their predecessors and they focus their effort on acting on the numbers they get. They’re not a data analyst – their focus is to understand the meaning of the details so they can do their job. They assume that the data has been checked, rechecked, and vetted by lots of folks in advance of their receiving the content. They believe the numbers and they act on what they see.

Level 2: “Give me the details”

This person has been using canned reports, understands all the basic details, and has graduated to using data to answer new questions that weren’t identified by their predecessors.  They need detailed data and they want to reorganize the details to suit their specific needs (“I don’t want weekly revenue breakdowns – I want to compare weekday revenue to weekend revenue”).  They realize the data is imperfect (and in most instances, they’ll live with it).  They want the detail.

Level 3: “I don’t believe the data — please fix it”

These folks know their area of the business inside/out and they know the data. They scour and review the details to diagnose the business problems they’re analyzing.  And when they find a data mistake or inaccuracy, they aren’t shy about raising their hand. Whether they’re a data analyst that uses SQL or a statistician with their favorite advanced analytics algorithms, they focus on identifying business anomalies.  These folks are the power users that are incredibly valuable and often the most difficult for IT to please.

Level 4: “Give me more data”

This is subject area graduation.  At this point, the user has become self-sufficient with their data and needs more content to address a new or more complex set of business analysis needs. Asking for more data – whether a new source or more detail – indicates that the person has exhausted their options in using the data they have available.  When someone has the capacity to learn a new subject area or take on more detailed content, they’re illustrating a higher level of data knowledge.

One thing to consider about the above model is that a user will have varying data knowledge based on the individual subject area.  A marketing person may be completely self-sufficient on revenue data but be a newbie with usage details.  A customer support person may be an expert on customer data but only have limited knowledge of product data.  You wouldn’t expect many folks (outside of IT) to be experts on all of the existing data subject areas. Their knowledge is going to reflect the breadth of their job responsibilities.

As someone grows and evolves in business expertise and influence, it’s only natural that their business information needs would grow and evolve too.  In order to address data usability (and project success), maybe it makes sense to reconsider the various user audience categories and how they are defined.  Growing data knowledge isn’t about making everyone data gurus; it’s about enabling staff members to become self-sufficient in their use of corporate data to do their jobs.

Photo “Ladder of Knowledge” courtesy of degreezero2000 via Flickr (Creative Commons license).
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