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Similar a Data Alchemy Overview Presentation (Static Version)
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Data Alchemy Overview Presentation (Static Version)
- 1. Eric King Director of Customer Solutions Mark Rubenstein Director of Client Services
- 2. Transforming Data Into Gold Transforming Data Into Gold
- 5. Issues to Unlocking Data Insights Resource & Productivity Gap THE CHALLENGE Analyst Resources Category Management Focus Amount of Data Data Analysis Requirements
- 9. Issues to Unlocking Data Insights Resource & Productivity Gap THE CHALLENGE Analyst Resources Category Management Focus Amount of Data Data Analysis Requirements
- 12. Flow of Data, Analysis and Update UPDATE Data Source/Component Updates and Bundling Tasks Can Be Automated DATAALCHEMY PROCESS STORE EXECUTION Consumer Behavior INTRANET, CD, E-MAIL, DISK Sales force, business planners, trading partners, & management E 1 72.7% 50.3% 53.5% 81.0% 11.0% 2 78.8% 43.9% 98.0% 11.0% 3.6% 3 64.7% 56.6% 36.9% 61.0% 59.4% 4 4.1% 79.9% 78.2% 84.6% 51.3% 5 1.0% 87.3% 58.0% 8.8% 59.2% 6 45.4% 64.2% 9.4% 1.9% 54.8% 7 64.3% 16.6% 80.8% 16.7% 12.9% 8 98.6% 100.0% 60.3% 7.5% 41.0% 9 28.2% 89.0% 60.3% 17.3% 40.7% A B C D CONSUMER DATA Multiple data sources IMPORT DataAlchemy is a SINGLE PLATFORM for disparate data types Import and manage data, including Direct POS, Shipments, Panel, IRI, Nielsen, etc . Update components CREATE COMPONENTS Component 1 Average Price Promo Price Sales Volume Promotion by Season Promotion by Brand/Flavor Promotion by SKU Component 2 Dollar Sales Unit Sales Volume Sales Component 3 Dynamic charts, graphs, tables, spreadsheets Project, output and other files BUNDLE
- 25. DataAlchemy Data Management Demonstration
- 27. Sample Using Segmentation, Lists & Aliases DATAALCHEMY SAMPLE
- 28. Sample Using Segmentation, Lists & Aliases DATAALCHEMY SAMPLE
- 29. Sample Using Segmentation, Lists & Aliases DATAALCHEMY SAMPLE
- 30. Sample Using Segmentation, Lists & Aliases DATAALCHEMY SAMPLE
- 31. Sample Using Segmentation Hierarchy, Lists & Aliases DATAALCHEMY SAMPLE
- 32. Sample Using Segmentation Hierarchy & Dynamic Time Period List DATAALCHEMY SAMPLE
- 35. Excel Sample Using Segmentation & Dynamic Time Period List DATAALCHEMY SAMPLE
- 37. What’s Next For DataAlchemy?
- 39. Thank You alqemyiQ Corporation 959 Concord Street Framingham, MA 01701 877-722-3988 or 508-626-7511 [email_address] www.alqemyiqcorp.com
Notas del editor
- First, I would like to take a few minutes to introduce our organization for those of you who may not yet be familiar with alqemyiQ and discuss some of the challenges faced by many organizations that DataAlchemy can address. alqemyiQ Corporation , formerly known as Kenosia has been in business since 1999 developing fact-based selling, data analysis and presentation software solutions. We have a strong presence with many top tier consumer packaged goods clients, as well as several CPG broker services organizations. alqemyiQ is committed to customer satisfaction and offers professional support, training and consulting services. In a moment, you will see the awards we have earned that speaks to our customers’ satisfaction Our company was re-branded as alqemyiQ in 2009 to support our new vision and investment. At the end of this webinar, I will briefly touch upon the new developments that are now underway. alqemyiQ has also become a Process Software company. Process Software is a trusted brand that has delivered reliable networking and security solutions to mission-critical enterprise businesses (across all industries) for over 25 years. The company's longevity and experience provide a solid foundation for alqemyiQ's growth and cutting edge technology. DataAlchemy, our award-winning data analytics and presentation solution continues to evolve to meet our client’s increased needs. DataAlchemy has won several Reader’s Choice awards from Consumer Goods Technology Magazine during the past several years.
- As I mentioned earlier, many top CPG manufacturers and service organizations rely on DataAlchemy to support their data analytics reporting and presentation efforts. This is just a sampling of our valued clients. In a recent article published in Consumer Goods Technology magazine, Heineken reported a 70% reduction in the time spent managing data using DataAlchemy, which has resulted in more time available for analysis and insights upon which they can act. As a DataAlchemy developer at Cadbury for 8 years before joining alqemyiQ, I was able to achieve similar time savings, which greatly increased the speed at which current information was provided to sales colleagues, broker partners and senior management.
- One of the key challenges many organizations face is how to unlock insights from the data they receive from one or more providers. This is particularly challenging due to the increased focus on category management and the significant increase in sources of data that must be managed to meet analysis requirements. However, in many cases, the amount of resources available has not kept pace with the increased data demands, resulting in a resource and productivity gap. DataAlchemy is designed to fill this gap by providing a common platform through which disparate data sets can be managed and used to produce dynamic reports and presentations that help users develop insights.
- As you can see, there are various types of data that multiple decision makers are asked to use to run their business. Each of these sources of data have their own tool and interface that must be learned and used efficiently in order to mine the data needed. The problem with this model is that it is very difficult, if not impossible for one person to learn how to access and manage all these databases. As a result, silos of data reside in an organization. And there is an obvious danger in having only one expert per tool. In addition, if you want to change a certain tool that it is used, re-training is required and this can result in a large expense to the organization. Finally, decision makers can’t bring all this data together in one platform and the result is information chaos. Current software solutions can’t bring it together to provide one picture of what is happening in the supply/demand chain. CPFR (Collaborative Planning, Forecasting and Replenishment ) ERP (Enterprise Resource Planning
- The DataAlchemy platform can bring these data sources together and bring order to the chaos. Decision makers at the center of the data storm will be able to make better decisions more quickly with more data.
- As I mentioned earlier, many top CPG manufacturers and service organizations rely on DataAlchemy to support their data analytics reporting and presentation efforts. This is just a sampling of our valued clients. In a recent article published in Consumer Goods Technology magazine, Heineken reported a 70% reduction in the time spent managing data using DataAlchemy, which has resulted in more time available for analysis and insights upon which they can act. As a DataAlchemy developer at Cadbury for 8 years before joining alqemyiQ, I was able to achieve similar time savings, which greatly increased the speed at which current information was provided to sales colleagues, broker partners and senior management.
- One of the key challenges many organizations face is how to unlock insights from the data they receive from one or more providers. This is particularly challenging due to the increased focus on category management and the significant increase in sources of data that must be managed to meet analysis requirements. However, in many cases, the amount of resources available has not kept pace with the increased data demands, resulting in a resource and productivity gap. DataAlchemy is designed to fill this gap by providing a common platform through which disparate data sets can be managed and used to produce dynamic reports and presentations that help users develop insights.
- As you can see, there are various types of data that multiple decision makers are asked to use to run their business. Each of these sources of data have their own tool and interface that must be learned and used efficiently in order to mine the data needed. The problem with this model is that it is very difficult, if not impossible for one person to learn how to access and manage all these databases. As a result, silos of data reside in an organization. And there is an obvious danger in having only one expert per tool. In addition, if you want to change a certain tool that it is used, re-training is required and this can result in a large expense to the organization. Finally, decision makers can’t bring all this data together in one platform and the result is information chaos. Current software solutions can’t bring it together to provide one picture of what is happening in the supply/demand chain. CPFR (Collaborative Planning, Forecasting and Replenishment ) ERP (Enterprise Resource Planning
- The DataAlchemy platform can bring these data sources together and bring order to the chaos. Decision makers at the center of the data storm will be able to make better decisions more quickly with more data.
- This slide will demonstrate the typical DataAlchemy process that tracks the flow of data through the import, analysis, distribution and update steps. Data is obtained from several methods. In this example, we are using retail consumer behavior that results in the typical syndicated data sets. This data is imported into DataAlchemy, which can serve as a single platform for various data types. As we will demonstrate shortly, various structures may be created to organize and manage this data within DataAlchemy, after which analytic components, such as graphs, tables, spreadsheets may be created within a project file. This project file is bundled for distribution via one of several methods and sent to end users. End users can then use the interactive features of these reports to gain insight into their specific areas or accounts and implement tactics to address strengths and opportunities. When updated data is received, it is imported into DataAlchemy and all components may be automatically updated without the need to recreate them. The project is quickly bundled and redistributed to end users. Most of these process steps can be automated within DataAlchemy to add efficiency. For example, once updated data is obtained, automated scripts can update DataAlchemy data sources, update project files, update PowerPoint and Excel reports and bundle the projects for distribution to end users.
- DataAlchemy offers a flexible data import process that can handle a variety of data layout and file formats. These includes common IRI and Nielsen layouts, tabular layouts, Wal-Mart Retail Link and xls, xlsx, csv, txt file formats. The new Data Import Wizard introduced in the current version of DataAlchemy provides even more flexibility by now allowing users to import data from MS-Access files and other non-standard file formats, such as those received in retailer POS data files. When necessary, custom import scripts can be provided to handle specialized data import requirements.
- With this as a background, the primary topic of today’s discussion is how DataAlchemy’s data management features facilitate report development and analysis. Once data is imported into DataAlchemy, several data management features allow users to create structures that enable faster and easier development of analytical charts and reports. These include segmentation, segmentation hierarchies, aliases, lists, aggregates and custom measures. Let’s take a few moments to review each of these features.
- Segmentation allows the user to better organize data and is particularly useful when importing granular data, such as store and UPC level information. For example, products imported at the SKU level can be easily segmented to produce brand, flavor, pack type, pack size and other subtotals for faster and more effective analysis. Store level data can also be segmented by region, state, store type, etc. As new data is imported, it may be easily added to new or existing segmentations. Segmentation can be automatically imported from data files or manually created within DataAlchemy. Segmentation can be created within products, geographies and time periods. Segmentation allows users to quickly create reports that compare brands, flavors, sizes, markets, store types, etc. to gain insights regarding performance. Once segmentations are created, its components can easily be reassigned if necessary
- Once segmentations are created, segmentation hierarchies can be quickly generated to provide a drill-down view of the data. Multiple segmentation hierarchies can be created to offer different drill-down views. These hierarchies make it easy to compare products at similar levels of the hierarchy.
- Lists can be created that allow the user to quickly select multiple items when building reports. In addition, when lists are used to build reports, any changes to a list will automatically be applied when reports are updated. This saves time by allowing the user to make quickly make changes to multiple reports at once. Dynamic lists may be built in the time period dimension to ensure that all reports and presentations using these lists will always reflect the latest periods. Using lists to build reports also allows end users to make global selections that will automatically be applied to all reports or presentation pages. Imagine a 10, 25, or 50 page presentation that can be quickly updated to reflect desired markets and time periods.
- Aggregates may be created in the geography, product and time period dimensions to get a single value. These aggregates may be additive to get a sum of its components or subtractive (e.g. Rest of Market).
- The alias function allows the user to shorten or clarify long or non-descriptive names so they may be more effectively used in reports and presentations. Aliases may be imported from a flat file or manually created within DataAlchemy. Aliases may be applied to any item in the geography, product, measure and time period dimensions. Within the time period dimension, aliases may be automatically assigned one of several pre-defined formats. As new time periods are imported, these aliases will be automatically applied.
- Many types of custom measures may be built within DataAlchemy. Users can build metrics that are not available in the source data file and can save time by eliminating the need to pull some measures from the data provider. DataAlchemy custom measures can also resolve aggregation rule issues for certain measures. Custom measures can also be nested within other custom measures to obtain the desired results. Given the wide scope of custom measures, they will be the focus of a future webinar.
- DataAlchemy features robust graphic capabilities, including a wide variety of 2 dimensional and 3 dimensional chart options. A Chart Designer module offers numerous options to customize the appearance of charts. In addition, users can create and save chart templates with the desired options. These templates can then be chosen for future charts. DataAlchemy also provides the ability to develop multiple chart views that may be quickly toggled between during a presentation.
- DataAlchemy features an Excel Add-In features that allows users to develop interactive Excel reports Once data is extracted from DataAlchemy into Excel, users can also use Excel’s native charting options Excel reports and charts are quickly updated as new data is received
- Let’s take a view moments to demonstrate how easily some of these data management features may be used. I will now switch to the DataAlchemy application in which I have already imported data into a sample data source that will be used to develop analytic charts.
- In this first example, I used the brand segmentation as well as the time period list and aliases created in DataAlchemy to create two dynamic charts.
- In this example, I quickly created the same chart, but used the flavor segmentation.
- This is another example using the brand segmentation with different measures to analyze promotional information. I also created another view of this data using the package type segmentation.
- Similar to the previous example, this chart provides two view of the data, one at the brand segmentation level and the other at the package type level.
- This example illustrates the use of the segmentation hierarchy feature that allows the user to drill down within segments to view the desired information. In this example, the user can drill down to the flavor level.
- This chart also illustrates the use of the segmentation hierarchy feature. However, in this example, the user can drill down to the SKU level.
- DataAlchemy also allows you to use it’s data management features to create interactive Excel-based reports.
- Now I’d like to talk about where we are going with DataAlchemy’s continued development. As I mentioned earlier, organizations now require more data from multiple sources and they need it more quickly than ever before. We are now building architecture around this trend. Our vision is to take the presentation and cube creation layer which is DA today with its data management and dynamic reporting and presentation capabilities and add a backend architecture that will meet your increased data demands now and in the future. Our plan is to marry DataAlchemy’s current presentation and cube creation layer that I demonstrated earlier with a Data Signal Repository (DSR), which is essentially a very large database into which many different types of information can be loaded. The idea is that different processing agents will operate on each data source and are responsible for bringing in the data into the DSR as they become available. The DSR will stage and run automated processes to cleanse and normalize the data so it can be accessed when needed by the DataAlchemy cube and presentation layer that currently exists. To differentiate this product, we have also added an access control layer that will allow your organization to determine what data sets (or applications) are being used, who is using them and how often. This way, you will be able to determine if the data source or application is adding any value to your organization. The benefit is you can identify data sets and/or applications for which you are paying but are not used. Conversely, if you see a lot of value in an information source or application, you may want to make sure it is being used. The key to this architecture is that it is open. We use industry standards and publish the application programming interface (API). This way you won’t be locked into our solution. You can continue to use your existing applications which may be a third party application or internal program. It will plug into alqemyiQ Enterprise. Therefore, you will not be locked into a technology that will require you to come to us.
- Thank you for you time and attention today. I hope today’s presentation helps you understand how DataAlchemy’s powerful and easy to use data management features can help bring order to data and help unlock insights that can drive your business. I’d now like to address any questions that may have come in during this webinar.