Harmonizing Data for the Warehouse
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Be certain data scientists can trust their data
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Harmonizing Data for the Warehouse
- 1. © 2013 Kalido I Kalido Confidential I June 5, 20131 Harmonizing Data for the Warehouse Be Certain Data Scientists Can Trust Their Data June 4, 2013
- 2. © 2013 Kalido I Kalido Confidential I June 5, 20132 Agenda Needs of the Data Scientist Assessing the data Load and integrate new data Stewardship is key to trust in data Make it available fast Demonstration
- 3. © 2013 Kalido I Kalido Confidential I June 5, 20133 Opportunities and Challenges of Data Scientists Data scientists need new data fast, but can’t sacrifice accuracy! Forward looking Requirements are murky Need data they can trust Frequently requires new data
- 4. © 2013 Kalido I Kalido Confidential I June 5, 20134 Assess the Data to Answer the Question Data Scientists need map to quickly assess the data they have now! What data is available? Is it all the data I need? How do I tie it together?
- 5. © 2013 Kalido I Kalido Confidential I June 5, 20135 New Data – New Insights New insights are the business of the data scientist. What other data improves my insights? Can I get it yesterday? Can you align it with the data I have? Will it be accurate?
- 6. © 2013 Kalido I Kalido Confidential I June 5, 20136 Data Scientist Sandbox Manage Sandboxes • Metadata • Trusted Data • BI Semantic Layers Kalido Information Engine Automation • ETL • Business Rules • Physical Layer • Stewardship • Sandbox Source Warehouse Business Information Model Define Requirements Graphically • Data Modeling • Metadata • Business Rules Agile DW: Define Data Science Needs, Shorten data integration time, Delivers New Insight Fast! Real Agile Data Labs and Sandboxes Workflow
- 7. © 2013 Kalido I Kalido Confidential I June 5, 20137 Business Information Modeling Captures requirements using business terms, not technical ones Clear view of warehouse artifacts Add artifacts by drawing them The model drives the solution “What sales channel did a customer on-board?” “What are the product mixes by channel?”
- 8. © 2013 Kalido I Kalido Confidential I June 5, 20138 Kalido Product Demonstration Demo
- 9. © 2013 Kalido I Kalido Confidential I June 5, 20139 Demonstration Refactor the Business Model
- 10. © 2013 Kalido I Kalido Confidential I June 5, 201310 Demonstration Integrate & Steward New DataWorkflow
- 11. © 2013 Kalido I Kalido Confidential I June 5, 201311 Demonstration Automate Results Generation
- 12. © 2013 Kalido I Kalido Confidential I June 5, 201312 Key Kalido Information Engine Capabilities Sophisticated Modeling High Performance Data Loading and Integration Data Matching Workflow Data Authoring Interface Comprehensive Hierarchy Support Powerful Results Generation Inexpensive Change Management Security & Audit High Performance Platform Support
- 13. © 2013 Kalido I Kalido Confidential I June 5, 201313 For More Information Visit http://get.kalido.com/harmonize to... Check out our end-to-end demonstration series Tune in for our next Data Scientist Summer Session: Rapid Iteration Methodology Using Modeling Get the Business Information Modeler Request a demo
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- As we learned fromArt Krulish on Tuesday, omni-channel marketing presents a set of new challenges for retailers--and their data warehousing professionals.<Click>Today’s highly-connected marketplace offers more choices, which makes understanding customers more important than ever. And customers realize this too. In fact, many are now expecting companies to cater to them like never before.<Click>Smart devices and social media sites have up opened up new ways for customers to interact with retailers. The result is spreading customer interactions across many channels. A key task for data professionals is to figure out which channels contribute most to a reliable view of their customers.<Click>And with these new channels comes lots and lots of data. Some of the data may come in semi-structured forms that are unfamiliar to the data professional. Figuring those out, and the cost of processing large volumes of it, makes it critical for data professionals to be certain they are crunching only the data that will improve customer insights.<Click>Some of this information is fleeting. If I stop in at a coffee shop, I’ll be in the area long enough to log onto WiFi, check-in on Facebook, and drink my latte—then I’ll be off. If I get an offer that night to check out the gym next door, it’s probably too late. Unless I visit that coffee shop several times a week. Detecting patterns in customer behavior separates the noise from real opportunity. <Click>But in order to get to those trends, we need both short and long term views—in short we need an enterprise view of the customer.
- As we learned fromArt Krulish on Tuesday, omni-channel marketing presents a set of new challenges for retailers--and their data warehousing professionals.<Click>Today’s highly-connected marketplace offers more choices, which makes understanding customers more important than ever. And customers realize this too. In fact, many are now expecting companies to cater to them like never before.<Click>Smart devices and social media sites have up opened up new ways for customers to interact with retailers. The result is spreading customer interactions across many channels. A key task for data professionals is to figure out which channels contribute most to a reliable view of their customers.<Click>And with these new channels comes lots and lots of data. Some of the data may come in semi-structured forms that are unfamiliar to the data professional. Figuring those out, and the cost of processing large volumes of it, makes it critical for data professionals to be certain they are crunching only the data that will improve customer insights.<Click>Some of this information is fleeting. If I stop in at a coffee shop, I’ll be in the area long enough to log onto WiFi, check-in on Facebook, and drink my latte—then I’ll be off. If I get an offer that night to check out the gym next door, it’s probably too late. Unless I visit that coffee shop several times a week. Detecting patterns in customer behavior separates the noise from real opportunity. <Click>But in order to get to those trends, we need both short and long term views—in short we need an enterprise view of the customer.
- As we learned fromArt Krulish on Tuesday, omni-channel marketing presents a set of new challenges for retailers--and their data warehousing professionals.<Click>Today’s highly-connected marketplace offers more choices, which makes understanding customers more important than ever. And customers realize this too. In fact, many are now expecting companies to cater to them like never before.<Click>Smart devices and social media sites have up opened up new ways for customers to interact with retailers. The result is spreading customer interactions across many channels. A key task for data professionals is to figure out which channels contribute most to a reliable view of their customers.<Click>And with these new channels comes lots and lots of data. Some of the data may come in semi-structured forms that are unfamiliar to the data professional. Figuring those out, and the cost of processing large volumes of it, makes it critical for data professionals to be certain they are crunching only the data that will improve customer insights.<Click>Some of this information is fleeting. If I stop in at a coffee shop, I’ll be in the area long enough to log onto WiFi, check-in on Facebook, and drink my latte—then I’ll be off. If I get an offer that night to check out the gym next door, it’s probably too late. Unless I visit that coffee shop several times a week. Detecting patterns in customer behavior separates the noise from real opportunity. <Click>But in order to get to those trends, we need both short and long term views—in short we need an enterprise view of the customer.
- The Kalido information Engine is the first highly-automated, purpose-built environment for implementing agile data warehouses.Business modeling allows IT and business to speak the same language as they collaborate because the requirements and business rules are defined graphically and then automated by Kalido.And because a Kalido warehouse is highly automated, things move quicker in development by reducing etl coding, removing the need for unnecessary translations into to logical and physical models, as well as having developers make changes to the physical layer directly.Finally, Kalido delivers information in a variety of formats, including industry standard BI tools like Excel, Qlik View, Business Objects, Cognos and SSIS. Automating the BI semantic layer greatly reduces the time required to build reports, improving your reaction time to customer trends.
- Let’s take a few moments to describe a business model in a little more detail. This model describes a brick and mortar electronics retailer. As you see, the model represents real world aspects of the business such as the Products, Customers and internal organization. These domains are drawn as tan square boxes. Within them are individual elements that describe these domains in more detail, such as credit rating and product class, which are drawn as light blue square boxes. Together they describe the reference data of the organization and they do so using business terms, not technical ones. For example, the similarities and differences between Corporate and Individual Clients is made clear and graphical. In other warehouse environments, these simple relationships could be fragmented across numerous normalized tables and, therefore not nearly this easy to interpret.The black lines you see describe the relationships and hierarchies of the organization. Solid and dashed lines represent mandatory and optional relationships. The lines that loop back to the same object denote a hierarchy within that object itself.The activities undertaken by the company, most commonly described as transactions, are represented by the rounded, colored boxes. Sales Revenue and sales projections from brick and mortar stores, for example. Lines of the same color as the transaction box connect it the reference data that are directly captured. From there, transactions are easily summarized up any of the reference data hierarchies, making it easy to get to analysis quickly using Kalido.Even as these models grow, they remain relevant to the business. They are the first place those users as well as data scientists look to understand what is possible with the data—and how new possibilities can be added. To show you how the business model drives the solution, let’s quickly demonstrate how this brick and mortar retailer would add it’s first new channel.
- --Staging Layer Definition--Integration Layer DefinitionNext I’ll show the consumption layer definitionDemo deploying the Corp Demo Model--BIM (deploy)--Explorer (show then build att & map tables)--Open SQLServer and show tables?
- (TD_DEMO_3)--Walk through the UID screens--Show the result modelFeel like it takes too long to generate the UID but am open to discuss.
- (TD_DEMO_3)--Walk through the UID screens--Show the result modelFeel like it takes too long to generate the UID but am open to discuss.
- The Kalido Information Engine offers many advanced capabilities, like:Comprehensive Hierarchy Support, which enables business and technical teams Express complex business relationships simply and graphically/History tracking and Audit, where Kalido maintains history of both the data AND the model. Kalido warehouse operations automation simplifies the process of automating loads, test switches and load dependencies in Kalido and move to scheduling toolThese capabilities make the Kalido Information engine the right warehousing platform for deriving the right analytics from your Omni-ChannelOrganization to fully understand your customers.Thank you for attending today’s event.