This post is from the Numerify blog and has not been updated since the original publish date.
IT Business Analytics - Build or Buy?
Whether the decisions IT leaders make concern revenue, costs, team productivity, devops, future risk, or just day-to-day service management, having data at their fingertips is key to optimizing those choices.
Decision-makers either have to build the analytics systems themselves or procure a solution from a reputable vendor. Both choices have their own set of pros and cons.
A complete IT business analytics solution gathers data from multiple sources, analyzes this data, and produces actionable visualizations that help leaders solve the business' ongoing challenges and pain points. These systems are strengthened when they include AI-driven models, machine learning, and predictive capabilities to identify patterns and insights that human eyes alone couldn't have detected.
Developing your own solution promises perfect-fit functionality, but risks overextending your team, contending with unexpected roadblocks, and compromising on functionality for the sake of conserving finite organizational resources.
On the other hand, buying an off-the-shelf solution implies reliability and fast time to market through a proven product and service model while allowing your team to conserve resources. However, this risks the opportunity cost of functionality that does not work seamlessly with how you run your operations.
Selecting a customizable version of a proven analytics product or service can ultimately provide the best of both worlds, providing the highest chance of solving an IT team's biggest challenges with minimal risk of wasted resources.
Ultimately, though, the question of whether to build or buy your IT analytics solution depends on your organization's needs and goals.
Developing IT Business Analytics Yourself
Developing a proprietary system of IT business analytics can prove tempting for a number of reasons, especially among experienced development teams or organizations that have a D.I.Y. culture. Building your own solution enables you to achieve the exact level of functionality you desire while fitting within your existing preferences and processes.
Some teams may even have some elements of a complete IT business analytics system in place, and it's tempting to build it out further into a more complete solution. For example, you may have some basic Key Performance Indicators (KPIs) and operational reports already available in your IT Service Management (ITSM) or software development systems of record. Another common example is teams that have dumped their data from across the IT systems landscape in an enterprise data lake in an attempt to make sense of it.
However, developing a proprietary analytics system requires a large amount of resources — more than many teams anticipate. The system must not only determine how to integrate data from multiple sources, but it must also utilize analysis models that provide an accurate representation of priority metrics. A poorly built model can also include common statistical biases, creating a skewed view of daily operations and KPI target progress.
Such a system also requires a large team and can add to the administrative burden an IT department faces. This can lead to costs and efforts spiraling beyond the predicted scope of the initial project.
Using a Productized IT Business Analytics Solution
For many organizations, a productized analytics approach is the best option, especially one that supports the majority of data sources out-of-the-box. A well-designed, productized analytics solution can alleviate much of the technical drain on IT resources. It can also provide proven and extensible pre-built data models as well as capabilities that would normally be outside the scope of an in-house project – such as the extraction, transformation and loading (ETL) of data from external sources.
Working with an established software vendor is less of a risk than relying on external consultants or even internal resources. A productized analytics solution will also have a proven track record of deployment within fixed costs and timescales, and customer references to demonstrate its business value
In a sense, a productized analytics approach for IT Business Analytics can offer the best of both worlds, providing best practices and proven technology along with the ability to customize, support and extend a platform to meet future requirements.
Finding the Ideal Fit
When it comes to IT Business Analytics, what may work for one team may not be ideal for another.
IT leaders should start with the scope and nature of their current business challenges, and use those to set goals and priorities. This initial work forms an outline that can depict the scope of the analytics solutions needed and what criteria can enable an apples-to-apples comparison between available options — including the option to build a solution from scratch.
When comparing options, IT leaders should look for a number of factors that can set a solutions provider apart. They can ask questions like:
- Are they an analytics thought leader?
- Do they have a repeatable methodology for achieving customer success?
- Does their service model provide the level of attention we need should a crisis arise?
- Do they operate through an open architecture, allowing our organization to essentially "own" the solution and use it as we see fit, rather than creating yet another silo?
IT teams looking for more guidance on arriving at their decisions — and more pertinent questions to ask — can learn more about choosing the right analytics solution for their organization in our Buyer's Guide. It provides all the tools you need to arrive at the right IT business analytics software choice for your unique demands.
Related White Paper: Visibility to Drive Digital Transformation – Why IT Needs a System of Intelligence
- What is and is not a System of Intelligence
- The benefits and approaches for building one
- Some of the key areas where such a System of Intelligence can help
- How to leverage the benefits of a System of Intelligence for your organization