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According to a recent Gartner study, it is predicted that by 2013, less than 10% of organizations will succeed in their first attempts at data governance.
An additional survey for the U.K.-based Business Application Research Center (BARC) found poor data quality to be the No. 1 hindrance for business intelligence projects.
Reference Data Management
Reference Data Management (RDM) is a relatively new offspring of MDM functionality to provide the processes and technologies for recognizing, harmonizing and sharing coded, relatively static data sets for "reference" by multiple constituencies (people, systems, and other data).
Such a system provides governance, process, security, and audit control around reference data mastering. The RDM system also manages complex mappings between different reference data representations across the enterprise. And most contemporary RDM systems provide a publish-subscribe model for the sharing of such reference data.
Many large enterprises have begun to make RDM their initial test case or proof-of-concept for their MDM evaluations. Concurrently, MDM vendors are rushing to market RDM solutions to apply an MDM approach for centralized governance, stewardship and control.
Clearly, managing "simple" reference data will prove to be a key sales entry point for large enterprises and their MDM vendors.
Additionally, RDM can be expected to become a "ramp up" point of entry for many organizations planning for customer, product master and other domains.
Both as an IT discipline and a commercial off-the-shelf software solution, RDM solutions are being brought to market at an increasing pace.
80% of time is spent on data preparation.
After you have clean data, that‟s when the fun starts.
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A Horizontally Scaled, Key-Value Database
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Most organizations underestimate the amount of work required for data governance, especially data quality.
Even if you have high-level commitment, organization structures and processes, you still have to get someone to actually do the work.
Who determines the data fields and business algorithms (data transformations) that need to be defined and documented?
Who prioritizes the list?
Who meets with all interested parties and gets agreement on the definitions?
Who makes the business decisions when there is the inevitable disagreement?
Who documents it all?
Who implements these definitions in the applications, databases, reports and BI tools?
This is a lot of ongoing work.
Often the right people are not assigned to the tasks, they are not given the proper authority, they don't have the time, or not enough people are assigned.
Another important data governance issue is that business definitions vary. Too many times people try to settle on a single definition of a business term for an entire enterprise and wonder why they fail.
The classic example is sales. From a simplistic viewpoint there should only be one definition of sales. But in a typical enterprise each business group has its own perspective -- and there are different points in the supply chain that have their own definitions.
Manufacturing may consider off-the-dock as sales, logistics may include inter-company transfers, while others will not. Marketing
may look at list price, sales may apply discounts (depending on their commission plans) and finance may apply returns and allowances.
Which definition is right? It depends. Data governance should establish the common definition for sales (or whatever you want to call each variant) for each appropriate business situation.
Then when people discuss sales they can select the appropriate definition for each business context.
One size does not fit all, but common definitions for a business
context can be established.
Data governance requires discovery, definition and documentation phases with a lot of discussions, interviews and meetings. Business and IT have to achieve a common and holistic understanding of what data is used and how it is used to make business decisions.
Business people representing different functions and processes need to agree on the data definitions and business transformations used to support business decisions.
This is a time consuming and people-intensive process. If you short-circuit this process it is unlikely the definitions will be accepted or used by the business.
You need to start with a project plan that identifies priorities, tasks, resources and responsibilities.
I have seen many cases where a list of all the tables and columns in the data warehouse or enterprise application were printed out and handed to the newly appointed data stewards. The expectation was that all the data stewards had to do was return to their cubicles and type the definitions of these items into documents. Does not work.
Take the time and do it right. You cannot go from not having any data governance program to having an enterprise-wide implementation overnight.
Samantha Finnegan (650) 276-9350
SAP HOSTS MOBILE AND "BIG DATA" STARTUP FORUM IN SILICON VALLEY
June 15, 2012 - As part of its continued focus on working with startup companies, today SAP is holding its second SAP Startup Forum. A gathering of select startups, the forum is a full day of collaboration, learning and competition around mobile technologies leveraging the SAP HANA platform to tackle "big data" challenges. This event follows the SAPPHIRE NOW conference where several startups took center stage in Orlando.
More than 30 startups are registered to attend the SAP Startup Forum, and 15 will pitch business cases and software solutions ranging from enterprise mobile sales enablement to digital content creation and management. Hosted by the SAP HANA platform team, the event gives participants the opportunity to spend time on the SAP Labs campus in Palo Alto where they can hear directly from and meet key SAP leaders, such as the heads of the SAP Development Center for mobile and SAP HANA. They can also explore customer and funding opportunities with venture capitalists in attendance.
Dr. Vishal Sikka, member of the SAP Executive Board, Technology & Innovation, will keynote the event. "Given the success of the first SAP Startup Forum in March, we added a startup track at SAPPHIRE NOW in Orlando which was very well received," said Sikka. "We're now offering entrepreneurs with mobile solutions the opportunity to leverage the SAP HANA platform as the technology foundation to tackle 'big data' issues. As SAP HANA approaches its first birthday on June 20, it is enabling companies big and small with the technology foundation to create real-time applications on 'big data.'"
The SAP Startup Forum series exposes SAP to some of the most innovative and disruptive companies in Silicon Valley. The forums are also the qualifying events for startups to engage more deeply with SAP via the SAP Startup Focus Program, which offers development and go-to-market support.
The next SAP Startup Forum will be held at SAP Labs in Waterloo, Canada, on July 11, 2012. It will highlight some of the best startups from Eastern Canada, including the Greater Toronto Area (GTA) and Waterloo regions. Plans are currently underway for upcoming forums in Bangalore, Beijing, Berlin, Paris, Seattle and Tel Aviv, with many more to come. The events are invite-only and organized jointly by the SAP Technology & Innovation, SAP Global Marketing and SAP Labs organizations, along with SAP Ventures, the independent venture fund of SAP AG and a catalyst for growth-stage enterprise IT companies worldwide.
Presenting the data to end users in a usable, friendly manner is important.
Visualization is defined as viewing graphically represented meaningful data.
As data is collected, stored and then analyzed, it needs to be presented in a way that it can be understood and digested.
Programs able to analyze big data can sometimes interpret the data and represent it in a visual display for easier consumption and/or to show results.
Data Serialization is converting a data structure or object state into a format able to be stored.
Serialization occurs after the data is collected and when it is being processed. As the data gets sorted and pushed around between systems, it may need to be stored.
During these steps, the data will require serialization and it will be based on the different languages and APIs.
We have been getting many questions about Hadoop.
Hadoop is an open source implementation (Apache software product).
Hadoop was developed to enable applications to work with thousands of computational independent computers and petabytes of data.
Enterprise Hadoop Solutions Wave 2012
SAP HANA: In-memory computing technology impacts velocity, volume and value
Analyze mammoth volumes of data in real time. Learn about the SAP HANA platform – our lightning-quick in-memory computing software – and see how this leading-edge technology can help you deliver insights from virtually any data source – at the speed of thought.
Oracle: Big Data for the Enterprise
Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data sources and analyze them alongside your existing data to find new insights and capitalize on hidden relationships. Learn how Oracle helps you acquire, organize, and analyze your big data.
At the end of the day, business people want to analyze their relevant data and extract insight to make better decisions.
With the exception of a few industries (finance, retail) it is much harder to achieve. Why?
Robert - great question. In my opinion, finance and retail are the low hanging fruit for bi and data analytics.
For other industries the bi - data tech is evolving and is now or will soon be good enough for the masses.
The problem, as I see it, is cultural. In my experience, the power of data and analysis over more traditional decision-making methods using judgment and intuition is a tough nut to crack. I think you need to merge both but many do not trust data, evidence and analysis - they are more comfortable with their biases.
How many people would predict the quality of wine based on climate and rainfall?
The use of randomized data in the world of business is hard for many to understand. Even for educated pros like doctors, the use of evidence and information in medicine rather than judgment is proving very difficult.
The use of sophisticated statistical techniques to analyze complex systems is simply a mind blower for many - even among the smartest and most educated.
Change management is the answer but it will be a long and hard war. I am more optimistic about the new upcoming generations - my teenage daughters will have no problem using sophisticated data analysis techniques.
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More startups are registered to attend the Forum, and 15 will pitch business cases and software solutions ranging from enterprise mobile sales enable to digital content creation and management.
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