IT risk is most commonly defined within the context of maintaining effective data and systems security, but there is another type of IT risk that executives manage daily: the risk of making a bad decision.
When you’re considering new tech, you and your staff may have little or no experience with these technologies. In these cases, it’s natural for IT decision makers to look at what other organizations are doing, and to make recommendations that fall in line with what appears to be working elsewhere. Taking these “proven” routes makes it easier to advance tech initiatives in budget funding discussions; this is also why executives look at leaders in tech markets, such as big data and analytics, when it’s time to makes decisions.
SEE: Big Data Policy (Tech Pro Research)
What IT leaders can learn
The list of top 10 big data companies based on revenue in 2015 is interesting, though it’s important to understand what is operating under the hood to see where and how companies are investing. Here are five things IT leaders can learn from the big data leaders, which includes HPE, IBM, Dell, Cisco, and others.
- Off the shelf best practice analytics solutions like IBM’s Watson, which has been customized for use in a variety of specific industry verticals, can bring immediate and tangible results in research, operational efficiencies, and strategies for organizations desiring a rapid return on their analytics investments.
- For companies choosing to invest in their own internal big data/analytics processing and storage, Intel-based “commodity” hardware is the most popular and cost-efficient platform.
- More enterprises are relying on their vendors and outside consultants because the companies lack in-house experts in certain areas.
- Tried-and-true analytics reporting (e.g., SAS and Cognos) that organizations have been using for years continues to play a significant role when it comes to analyzing data–in part because end users and IT are already familiar with these tools.
- New technology that is better suited for processing real-time analytics and that can use memory in new ways (e.g., SAP HANA) is transforming how physical servers and databases work.
In short, IT decision makers do not necessarily have to start from scratch when it comes to developing analytics for the business. The best approach is to tightly define the business cases that you want to bring analytics support to, and then look at your existing tools and see how far they can take you toward meeting these needs.
If you choose to build your own analytics operation in your data center, it can be done using hardware that your staff is already familiar with, although the hardware will need to be specially configured for analytics (not transaction) processing. This could mean changes in memory and processing and also different approaches to databases, such as using relational-graph database combinations.
For small to midsize businesses that fear they are being left behind in the analytics revolution, analytics platforms such as IBM Watson are now available in the cloud, which means you don’t have to purchase your own hardware and software, and you can access and use these resources by subscription. The cloud-based access helps level the playing field.
The ability to combine new and established technologies in the development of analytics eases the load for IT decision makers because they don’t necessarily have to invest in new resources every time they launch an analytics project. Proving the success of their analytics work also becomes easier, since staff and end users can use tools they have become familiar with. In addition, tech vendors deserve recognition for reinventing and repurposing many of their solutions to keep them relevant in the age of analytics.