Your top 10 checklist for IT
What should your IT department be working on right now?
When technology pundits published their 2012 predictions, no one was surprised to see topics like analytics, cloud, big data, mobile, social networking, virtualization and open source on their lists. Now that we're in the second quarter and reality is setting in, I want to take a look at hot tech trends from an IT perspective: i.e., what should IT shops be working on right now?
Use cloud technology as part of your analytics infrastructure. At SAS, we're seeing tremendous interest in our cloud-based analytic offerings, but we're also seeing considerable confusion. For instance, implementing a basic customer relationship management solution is simple and can be accomplished in a short time. But once you customize the out-of-the-box options (e.g., salesforce.com, Amazon, etc.), the magic of the cloud (easy implementation and low cost) is not so compelling. The point is that the cloud is not a panacea for operational analytics.
An analytics application is different from the typical application development life cycle, which consists of design, development, test and deployment. The analytics life cycle consists of an iterative analytics sandbox that requires flexibility, intense data cleansing and integration. Once the analyst has developed and tested the analytical models, the models are put into production and monitored. These models require consistent, reliable data. The analytics need to be embedded in operational systems to derive maximum value. This certainly doesn't mean that analytics can't be cloud-enabled; a significant number of customers do use the SAS cloud. However, IT and the business should work together to design the correct approach based on requirements.
Organizations need to move beyond reactive and silo-based approaches to a managed, even predictive approach that values information as a strategic asset.
Implement an analytics enterprise architecture approach. In many cases, the analytics environment is thought of as a separate data warehouse initiative that is not designed with the same rigor that is applied to operational applications. IT's role may be limited to managing the infrastructure and providing data support, an approach that typically leads to problems down the road. IT needs to understand the potential role and relevance of all architecture options that are open to them (e.g., in-database processing, in-memory, grid processing, etc.) and ensure that the proper choices are made when it comes to designing storage for multiple analytics projects to limit data sprawl. Finally, design an architecture that supports the entire analytics life cycle – data exploration and preparation, analytics sandbox support, and operationalizing the production environment.
Pilot a big data project. Start small while planning for the future. Consider working with your business counterparts to take on an unstructured data project, possibly using Hadoop as your data store for unstructured data. Or consider Hadoop for part of your ETL process as a vehicle for raw data, then using its distributed processing to extract relevant data into your data warehouse for additional analysis.
Take a strategic approach to data. As is typical with other technology disciplines, the data management market is undergoing constant change. Organizations use a variety of technologies to manage their data, including traditional ETL, data quality, MDM and data governance to ensure data is optimized for operational and analytical use cases. Organizations need to move beyond these reactive and silo-based approaches to a managed, even predictive approach that values information as a strategic asset. That strategic information management approach should support the entire information continuum spanning data, analytics and decisions.
Avoid the open-source hype. Having worked at both open-source and private-source software vendors, the only thing I have to say about the politics of open source is that people who think that open-source proponents are driven by altruism should spend time with open-source vendors. They are driven by the same business reality as private-source vendors. That being said, there is a place for both business models and IT has to make decisions based on a combination of product maturity, functionality, risk, reliability, service, trained resources and cost.
Ensure the pieces of your analytics infrastructure work together. In many cases, organizations have individual analytic disciplines such as forecasting, optimization, text analytics and data mining for specific departmental business problems. Although valuable to the department, from an enterprise standpoint the value is somewhat marginalized. How does this affect IT? IT must deliver an analytics infrastructure capable of supporting multiple, enterprise-class analytics initiatives; help provide integrated data that is the basis for multiple, related analytics projects; and play an instrumental role in integrating the results in operational systems.
Deliver analytics value directly to the decision makers. Move away from treating analytics as a reactive, after-the-fact reporting and analysis mechanism. Rich analytics should drive call center interactions and be used in real time to detect potential fraud. Analytics should be embedded directly within the operational applications. IT needs to support an analytics infrastructure that provides Web-service-enabled analytics or capabilities that enable operational applications, regardless of where they are running. This also provides the ability to reuse analytic or data preparation processes from different systems, applications or deployment approaches.
Provide access to meaningful analytics. Users now expect advanced data and information visualization. They expect analytics embedded within operational systems; analytics and reporting available anywhere, anytime; and analytics solutions to track additional context about customers and prospects based on their social media use. Just as IT has moved to enable basic self-service reporting and business intelligence, IT should look to expand the infrastructure necessary to enable meaningful analytics.
Drive IT and business collaboration. Business and IT alignment keeps popping up. Regardless of where your organization is, it's imperative that IT take a leadership role in driving real collaboration toward achieving satisfied business users and business success. It's important to note that with analytics, it's not just business and IT – it's business, IT and the data analyst or scientist. These constituents need to work together to deliver effective analytics. Consider implementing a center of excellence or competency center, and focus on the role of data as the linchpin that pulls these teams together.
Train your IT talent in the basics of analytics. Many recent studies indicate organizations are struggling to find analytic expertise. With growth in demand, the talent gap is likely to worsen – leaving IT organizations struggling without a proper understanding of analytics. It isn't necessary for your IT resources to become trained statisticians, but they should have an understanding of the analytics life cycle and data requirements. Instead of devoting all of your training dollars and time to new IT tools and languages, why not commit some to analytics training?