Data Science: Not Just For Big Data

These days, Data Science and Big Data have become synonymous phrases. But data doesn't have to be big for data science to unlock big value. Kalido hosted a webinar intitled Data Science: Not Just for Big Data, with Darren Peirce, Kalido CTO, David Smith, Data Scientist at Revolution Analytics and Gregory Piatetsky-Shapiro, Editor, KDnuggets. They discussed what changes in Data Science with Big Data, what remains the same, and suggested ways for getting best value from data regardless of the size. Watch and enjoy!

The Business Value of Master Data Management

Tomorrow, October 16th at 3PM ET, will happen a live Web broadcast presentation entitled The Business Value of Master Data Management,provided by DM Review and hosted by Eric Kavanagh with Jim Ericson, in its program called DM Radio.



According DM Review: "Achieving the coveted 360-degree view of the customer -- or even of a product, line of business or other entity -- is more possible than ever these days, thanks in large part to the maturation of Master Data Management. With advances in automated data quality and matching software, coupled with ever-faster data delivery mechanisms, this relatively new discipline is taking the information management industry by storm.

Unlike traditional, tightly coupled information systems, MDM solutions use a loose coupling of enterprise applications and a master data hub to deliver near real-time master records about customers, products, locations and other dimensions. These MDM solutions help improve the efficiency of enterprise systems by managing the maintenance of master records. This improves overall data quality, because master data records need not be maintained multiple times throughout the enterprise.

Tune into this episode of DM Radio to learn how MDM solutions are helping organizations align their information systems with business goals and strategies. We'll talk to Jill Dyche of Baseline Consulting, Darren Peirce of Kalido, Judy Ko of Informatica, and Anurag Wadehra of Siperian.

Attendees will learn:
- How MDM can yield significant business value
- The basics of Customer Data Integration
- The fundamentals of Product Information Management
- Why data quality must be “baked in” as opposed to “bolted on”
- Trends in MDM design and deployment."

In the DM Review website, you can register for this live Web broadcast.

You also can check out the DM Radio archives to hear previous programs with a variety of other issues.

The Master Data Management is one of the most important issues for companies nowadays. According Gartner: The truth is that achieving a single view across the enterprise, is key to running your business. The effective management and governance of master data is both an opportunity and challenge for many large enterprises. MDM poses unique challenges and requires new relationships between business and IT in areas such as workflow, governance, stewardship and data integration. To be successful, organizations must understand the role of information governance and the impact MDM has on their applications portfolio and information infrastructure. Organizations use MDM to accelerate enterprise agility, promote operational efficiency, achieve competitive differentiation, support enterprise transparency, make SOA work more effectively, and ensure corporate compliance.

The Power of Dynamic BI

TDWI published last week, a Q&A with Cliff Longman, CTO of Kalido, by James E. Powell, called The Power of Dynamic BI.

Cliff Longman did several interesting comments:

- Dynamic BI is business intelligence that is adaptable enough that it can deliver answers to business questions in time for those answers to be useful.

- How dynamic your BI needs to be depends on the business questions asked of it. Some questions might need answers within an hour, others within a month. Your current BI infrastructure may be able to answer some questions while others will push the infrastructure to a breaking point.

- Dynamic BI goes beyond traditional BI, delivering answers not only to the questions the business asked when the BI system was first installed, but also to new questions that come up daily: How does our company look post-merger? What would happen if we changed our sales territories? Is my current marketing promotion having an impact on sales?

- Many or most traditional BI implementations have difficulty delivering dynamic BI, since every change requires some reconfiguration of the data structure behind the BI tool. Often, by the time the system is ready to provide answers, it's irrelevant -- the opportunity has passed or decisions have already been made.

- It's often difficult for IT and business managers to ensure that the BI infrastructure truly reflects the business model. That's partly because business managers can't easily understand a data model, and therefore can't validate it or suggest changes. As a result, data warehouses are often built in ways that do not reflect the business.

- Even when the data structure reflects the business, the data warehouse infrastructure supporting this structure is rigid and difficult to change.

- Business modeling tools capture the way a business works in a medium that can easily be understood by both business managers and IT. With translation barriers eliminated, BI projects are more likely to address the needs of business managers out of the gate, and can easily be modified to address future changes.

- If the data structure is actually based upon and driven by the business model, then changes to the business model can be automatically reflected in the data structure. This limits the issues that come with changing a data warehouse's schema, and speeds the delivery of BI system answers to new business questions. This approach makes business intelligence far more dynamic.