So, this blog is dedicated to stepping outside the comfort zone once again and into the world of chaos. Not only may you not want to persist in your data quality transformations, but you may not want to cleanse the data.
Current thinking: Purge poor data from your environment. Put the word “risk” in the same sentence as data quality and watch the hackles go up on data quality professionals. It is like using salt in your coffee instead of sugar. However, the biggest challenge I see many data quality professionals face is getting lost in all the data due to the fact that they need to remove risk to the business caused by bad data. In the world of big data, clearly you are not going to be able to cleanse all that data. A best practice is to identify critical data elements that have the most impact on the business and focus efforts there. Problem solved.
Not so fast. Even scoping the data quality effort may not be the right way to go. The time and effort it takes as well as the accessibility of the data may not meet business needs to get information quickly. The business has decided to take the risk, focusing on direction rather than precision.
As the new analyst on the block at Forrester, the first question everyone is asking is, “What research do you have planned?” Just to show that I’m up for the task, rather than keeping it simple with a thoughtful report on data quality best practices or a maturity assessment on data management, I thought I’d go for broke and dive into the master data management (MDM) landscape. Some might call me crazy, but this is more than just the adrenaline rush that comes from doing such a project. In over 20 inquiries with clients in the past month, questions show increased sophistication in how managing master data can strategically contribute to the business.
What do I mean by this?
Number 1: Clients want to know how to bring together transitional data (structured) and content (semi-structured and unstructured) to understand the customer experience, improve customer engagement, and maximize the value of the customer. Understanding customer touch points across social media, e-commerce, customer service, and content consumption provides a single customer view that lets you customize your interactions and be highly relevant to your customer. MDM is at the heart of bringing this view together.
Number 2: Clients have begun to analyze big data within side projects as a way to identify opportunities for the business. This intelligence has reached the point that clients are now exploring how to distribute and operationalize these insights throughout the organization. MDM is the point that will align discoveries within the governance of master data for context and use.
Broadens the definition of metadata beyond “data on data” to include business rules, process models, application parameters, application rights, and policies.
Provides guidance to help evangelize to the business the importance of metadata, not by talking about metadata but by pointing out the value it provides against risks.
Recommends demonstrating to IT the transversality of metadata to IT internal siloed systems.
Advocates extending data governance to include metadata. The main impact of data governance should be to build the life cycle for metadata, but data governance evangelists reserve little concern for metadata at this point.
I will co-author the next document on metadata with Gene Leganza; this document will develop the next practice metadata architecture based partially but not only on a metadata exchange infrastructure. For a lot of people, metadata architecture is a Holy Grail. The upcoming document will demonstrate that metadata architecture will become an important step to ease the trend called “industrialization of IT,” sometimes also called “ERP for IT” or “Lean IT.”
In preparation for this upcoming document, please share with us your own experiences in bringing more attention to metadata.
The following question comes from many of our clients: what are some of the advantages and risks of implementing a vendor provided analytical logical data model at the start of any Business Intelligence, Data Warehousing or other Information Management initiatives? Some quick thoughts on pros and cons:
Leverage vendor knowledge from prior experience and other customers
May fill in the gaps in enterprise domain knowledge
Best if your IT dept does not have experienced data modelers
May sometimes serve as a project, initiative, solution accelerator
May sometimes break through a stalemate between stakeholders failing to agree on metrics, definitions
May sometimes require more customization effort, than building a model from scratch
May create difference of opinion arguments and potential road blocks from your own experienced data modelers
May reduce competitive advantage of business intelligence and analytics (since competitors may be using the same model)
Goes against “agile” BI principles that call for small, quick, tangible deliverables
Goes against top down performance management design and modeling best practices, where one does not start with a logical data model but rather
Defines departmental, line of business strategies
Links goals and objectives needed to fulfill these strategies
Defines metrics needed to measure the progress against goals and objectives
Defines strategic, tactical and operational decisions that need to be made based on metrics
Our latest BI maturity survey results are in. We used exactly the same questions from our online BI maturity self assessment tool to survey over 200 Forrester clients. Now you can compare your own BI maturity level against your peers by using data from the survey.
In the self assessment tool and in the survey we ask over 30 questions in the following 6 categories
Data and technology
Our clients rated themselves on the scale of 1 to 5 (5, if they strongly agree with our statement or 1, if they strongly disagree). Here are the overall results. Keep in mind that these results do not evaluate BI maturity accross ALL business, but rather in businesses that are already pretty far ahead in their BI implementations (they are Forrester clients, they read our research reports, they talk to our research analysts):