Friday, February 24th, 2012
Title: Business Intelligence – The Next Big Thing (Really!)
Tuesday, May 8, 8:00-9:00 a.m.
Speaker: John Bair, CTO, Ajilitee
Advances in technology are leading us to rethink our whole approach to BI – what data we can manage, how we manage it, and ways to use the resulting information. In this session we will explore three of the most important trends in BI: big data, advanced analytics, and cloud computing technologies – and the implications to your BI and information management programs.
You don’t have to be an e-commerce company with petabytes of data to take advantage of these technologies. If your business or IT organization, like most, perceives that your data keeps growing, that your legacy BI platforms struggle to keep up, or that the lack of information is a business disadvantage, then big data, analytics, and cloud should be on your radar, regardless of the maturity of your BI programs. Although widely hyped, advanced analytics, big data and cloud computing are nonetheless emerging as tools to enable new applications, with lower barriers to use. As a result, these technologies are driving a renewed visibility of BI in the minds of business leaders. This session will share lessons learned by early adopters. It will discuss applications, architecture alternatives, key challenges encountered, and the resulting implications for BI strategies and programs aimed at transforming businesses of all sizes.
You Will Learn
- Why big data, advanced analytics and cloud computing technologies matter
- Architectures for building new business analytic services
- Strategies for moving from passive to active analytics
- Approaches for helping your business stakeholders lead with BI
Visit the conference website>>
Wednesday, February 22nd, 2012
Title: Become a Power Data Steward: Trim the fat and balance tasks to operate with greater power and efficiency
Speaker: Tina McCoppin, Partner, Ajilitee
June 25-28, San Diego, California
Data Stewards have it tough. Pulled in multiple directions, they often juggle too much and fall prey to scattered responsibilities without generating lasting, impactful results. Similar to being on a yo-yo diet, Data Stewards often find themselves either going strong or losing steam.
To strike the right balance of effort for sustained results, we recommend a Data Steward Health Plan that blends both cardio and weight training for data stewards to go the distance in their role. In other words, Data Stewards should blend a focus on standards and conformity, metadata, enterprise glossaries and data dictionaries (“cardio”) with strength training to develop the skeletal muscles of your organization in such areas as enterprise data integration (EDI), data quality (DQ), and master data management (MDM).
This pump-it-up session will pinpoint activities that really count for Data Stewards. The Data Steward Health Plan is designed to trim the time wasters (or “fat”) and build cardiovascular endurance and muscle strength for optimal efficiency and results. We also will cover the latest specialized equipment (tools and frameworks) needed to target specific muscle groups and types of (data) movement to support your Power Data Steward training.
This session will detail:
- Primary/secondary responsibilities of a Data Steward
- How and where to “trim the fat”
- The Data Steward Health Plan: a week-by-week, month-by-month program to shed the weight and power through your job
- Practical tips for time management
- Must have tools for transparency and visibility
- Real-world examples
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Wednesday, February 22nd, 2012
Many organizations struggle to measure the value of data governance. Healthcare payers are no exception. The good news: there’s no shortage of data points to measure the impact and effectiveness of data governance.
The right combination of proven quantitative and qualitative metrics will help improve your information, and also will drive continued executive support for your program. Please enjoy our complimentary guide.
DOWNLOAD: The Ultimate Guide to Data Governance Metrics for Health Payers: 30+ Ways to Discover and Score Success in 2012
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Monday, February 20th, 2012
Some companies limit data profiling to a tsunami of SQL queries by analysts. This ‘non-scalable’ approach consumes a lot of time and is a tedious and uninspiring activity for a skilled analyst. Most important, this approach does not enable the groupthink of data profiling reviews. For that, we need an accelerator – and a quorum of in-house experts.
Vendors have come to market with toolsets that make it virtually inexcusable to run massive manual SQL checks to profile data. These data quality analysis accelerators provide an effortless and consistent set of data heuristics at the click of a mouse. The tools offer an ad hoc capability to see the data that is both broad and deep. Here are just a few of the items that can be validated: unique values, domain and range of values, default values, data types, field formats, outliers, codeset validity, presence of nulls, blank data, and invalid characters.
The data profiling tools are the accelerator, but the real value comes from the meeting of minds at data profiling review sessions.
Data profiling review sessions need a quorum of business and IT participants. The analyst(s) who wrote the source target mappings and data requirements needs to attend. Invite business SMEs that know and use the data regularly. A QA representative should attend to clarify issues, log the issues into a tracker or enterprise quality tool, and track issue resolution over the coming days.
Sessions are generally guided by the analyst. The source target mappings and business requirements documents should be close at hand during the session for reference. The most useful review sessions have live connections to the data profiling tool and to the data sources. Questions that pop up during the review sessions can be addressed in more detail by drilling into the data profiler, and/or by querying the source data.
Send out the link to the data quality profiling results at least a day before the review session so that everyone has a chance to do reasonability checks. Distribute a checklist of generic data quality pointers. The checklist will direct attention to key fields like primary keys and fields required by the business reports. This will also help ensure a consistent approach to the effort.
Witnessing the groupthink in these sessions is fascinating. Each participant comes to the meeting with a unique point of view on the project inputs and outputs. The data profiling results are parsed to answer questions, generating new questions. A quick exploration back into the source system provides some immediate answers. The data mappings and business requirements are validated. New business rules are proposed on the spot to solve observed issues. The roundtable discussion can be rich and fruitful. This is one forum where the whole is certainly greater than the sum of the parts.
Automated data profiling has become so effortless that we need to consider checking data quality at many points in the development lifecycle – during source analysis, when source data is landed, and after business rules are applied. The data profiling exercise need not be limited to single files or tables. Many profiling tools have inter-table profiling capabilities that can help validate referential integrity and find orphan keys.
Analysts should be analyzing data profiling output, not writing and running endless queries. The delivery team reaches a new level of collaboration when the tools and processes to enable data profiling are part of the team mindset.
