data warehousing

 

Every self-respecting data management professional knows that “business alignment” is critical to the success of a data and analytics program. But what does business alignment really mean? How do you know if your program is aligned to the business?

Before describing what business alignment is, let me first list what it is not:
• Interviewing end users to understand their needs for data and analytics
• Recruiting a highly placed and influential executive sponsor
• Documenting a high return on investment
• Gaining agreement on the data strategy from multiple business areas
• Establishing a business-led data governance program
• Establishing a process to prioritize data requests and issues

It’s not that the items on this list are bad ideas. It’s just that they are missing a key ingredient that, in my experience with dozens of clients, makes all the difference. None of these items are even the best first step in developing a data strategy.

So what’s wrong with the list? Let me illustrate with an example. I was working with a team developing a data strategy for a large manufacturing company. We were interviewing a couple of high level managers in marketing, and it went something like this:

Me: What are some of the major business initiatives that you’re expected to deliver this year and next year? Do you have some thoughts on the data and analytics that will be needed within those initiatives?

Marketing manager: Sure, well, we have this targeted marketing initiative that we think will be a big winner. When a customer contacts us for warranty information, we think we can cross-sell products from another business unit… here’s a spreadsheet… we’ve calculated that this will bring back $14 million in additional revenue every year. We’re so excited that you’re doing the data warehouse initiative… We’ve been proposing this marketing idea for the last four years and haven’t been able to get it approved, and now we can finally get it done!

Me: I didn’t ask what you think the business initiatives should be; I asked you what they already are! (Ok, I really didn’t say it that way, but I wanted to.)

Why couldn’t they get the project approved? Who knows? Maybe the ROI was questionable. Maybe the idea wasn’t consistent with the company strategy and image. All that matters is that it was not approved, and hence makes for a lousy value proposition for a data and analytics program.

There is nothing wrong with proposing exciting, new “art of the possible” ways that data can bring value to the business. But an interesting proposal and an approved initiative are not the same thing. The difference is crucial, and data management leaders who don’t understand this difference are unlikely to be seen as trusted strategic advisors within their companies.

So what does it mean to be business aligned? Business alignment means being able to clearly state how deployment of data, analytics, and data management capabilities will directly support planned and approved (meaning funded) business initiatives.

So, the first step toward developing a successful data strategy is not to ask the end users what data they want. Instead, the first step is to simply find the top business initiatives. They are usually not hard to find. Very often, there are posters all over the place about these initiatives. There are a number of people in the organization you can check with to find top initiatives - the CIO, PMO leads, IT business liaisons, and contacts in the strategic planning department are examples of good places to start.

Then, you should examine the initiatives and determine the data and analytics that will be needed to make each initiative successful, especially looking for the same data needed by multiple projects across multiple initiatives. Core, enterprise data is usually needed by a diverse set of initiatives in slightly different form. For example, let’s say you work for a retailer and you identify approved projects for pricing optimization, labor planning, and marketing attribution. You can make a case that you will deploy the sales and product data these applications need, in the condition needed, in the time frame needed.

Proceeding further, you can propose and champion a series of projects that deliver the data needed by various initiatives. By doing this, along with establishing architecture and design principles of scalability and extensibility, you harness the energy of high-priority projects (instead of running away from it) to make your business case, add value by supporting the value of pre-vetted initiatives, and also build a foundation of integrated and trusted data step by step, project by project. Once this plan is established and in motion, you can accurately state that your program is absolutely needed by the business and you are also deploying data the right way – and you can also say that your program is officially business aligned.

Guest Blogger Kevin Lewis is responsible for Teradata’s Strategy and Governance practice. Prior to joining Teradata in 2007, he was responsible for initiating and leading enterprise data management at Publix Super Markets. Since joining Teradata, he has advised dozens of clients in all major industries.

 

About one year ago, Teradata Aster launched a powerful new way of integrating a database with Hadoop. With Aster SQL-H™, users of the Teradata Aster Discovery Platform got the ability to issue SQL and SQL-MapReduce® queries directly on Hadoop data as if that data had been in Aster all along. This level of simplicity and performance was unprecedented, and it enabled BI & SQL analysts that knew nothing about Hadoop to access Hadoop data and discover new information through Teradata Aster.

This innovation was not a one-off. Teradata has put forward the most complete vision for a data and analytics architecture in the 21st century. We call that the Unified Data Architecture™. The UDA combines Teradata, Teradata Aster & Hadoop into a best-of-breed, tightly integrated ecosystem of workload-specific platforms that provide customers the most powerful and cost-effective environment for their analytical needs. With Aster SQL-H™, Teradata provided a level of software integration between Aster & Hadoop that was, and still is, unchallenged in the industry.

 

Teradata Unified Data Architecture™ image

Teradata Unified Data Architecture™

Today, Teradata makes another leap in making its Unified Data Architecture™ vision a reality. We are announcing SQL-H™ for Teradata, bringing the best SQL engine for data warehousing and analytics to Hadoop. From now on, Enterprises that use Hadoop to store large amounts of data will be able to utilize Teradata's analytics and data warehousing capabilities to directly query Hadoop data securely through ANSI standard SQL and BI tools by leveraging the open source Hortonworks HCatalog project. This is fundamentally the best and tightest integration between a data warehouse engine and Hadoop that exists in the market today. Let me explain why.

It is interesting to consider Teradata's approach versus alternatives. If one wants to execute SQL on Hadoop, with the intent of building Data Warehouses out of Hadoop data, there are not many realistic options. Most databases have a very poor integration with Hadoop, and require Hadoop experts to manage the overall system - not a viable option for most Enterprises due to cost. SQL-H™ removes this requirement for Teradata/Hadoop deployments. Another "option" are the SQL-on-Hadoop tools that have started to emerge; but unfortunately, there are about a decade away from becoming sufficiently mature to handle true Data Warehousing workloads. Finally, the approach of taking a database and shoving it inside Hadoop has significant issues since it suffers from the worst of both worlds – Hadoop activity has to be limited so that it doesn't disrupt the database, data is duplicated between HDFS and the database store, and performance of the database is less compared to a stand–alone version.

In contrast, a Teradata/Hadoop deployment with SQL-H™ offers the best of both worlds: unprecedented performance and reliability in the Teradata layer; seamless BI & SQL access to Hadoop data via SQL-H™; and it frees up Hadoop to perform data processing tasks at full efficiency.

Teradata is committed to being the strategic advisor of the Enterprise when it comes to Data Warehousing and Big Data. Through its Unified Data Architecture™ and today's announcement on Teradata SQL-H™, it provides even more performance, flexibility and cost-effective options to Enterprises eager to use data as a competitive advantage.