Click to learn more about author Manikandan (Mani) Seetharaman.
Selecting technology is a challenging and complex task, particularly for global organizations. Screening available options that fit basic needs is the easy first step, but the real work lies in determining how far to advance from there. Multiple considerations need to be balanced, so having a roadmap to maximize corporate resources and make the best decisions is essential.
Eight Tips for Making Sound Technology Decisions
1. Validate the need
As is the case with many corporate initiatives, the focus when purchasing technology to manage data needs to be aligned with the organization’s vision or business strategy. This can’t be stressed enough for those organizations moving toward a data-centric environment. Alignment helps keep technology needs in perspective. And, by concentrating on organizational goals, all parties in the selection process have a guide by which to determine the level of investment to be made given each one’s stated business case.
In addition, most organizations can fit their tech requirements into three categories: core requirements, nice-to-have requirements, and value-adds – or what might be considered luxuries if the budget was of no concern. Once these important categories are established, weigh the features of each technology under review against the priorities to help determine each offering’s overall potential.
2. Understand the corporate strategy relative to the data-maturity model
What, specifically, is the company’s end goal? Evaluate the response against the company’s overall vision, but also in terms of the organization’s level of data maturity and its place along the horizon of digital transformation.
Global organizations at an infancy level of data-maturity face the toughest uphill battle if a full transformation is the end goal. Beyond that level are organizations whose IT departments will be the primary beneficiary of the new technology. Third are those whose new technology is geared for certain offices or lines of business, and then by multiple offices and multiple lines of business. Finally, a fully data-mature organization uses digital-transformation technology throughout the organization globally, monetizing the data collected as an added-value stream.
3. Assess internal capabilities
Determine how much technology development can be done internally. Perhaps a “lesser” off-the-shelf technology can more simply and more efficiently be added to an existing infrastructure to deliver the capabilities desired.
In other words, before making a technology purchase or investing in external resources, make sure the organization needs them. If so, identify potential technology candidates and conduct a cost/benefit analysis of each one against specific core, nice-to-have or value-add requirements. Sometimes companies discover that the front-runner in its analysis, for example, will be even more suitable if combined with internal capabilities for development within the organization. Be sure to take into account various combinations of what’s offered and what already exists.
4. Determine the technology’s intended use
Identify the technical requirements of the technology the company needs. Is its job to provide data, analyze data, cleanse data, or store data? What, exactly, will be its technical role within the organization? What is the data model?
This assessment is as critical as determining corporate goals and existing capabilities. Therefore, in the weighted assessment of multiple potential technologies, balance the requirements – core, nice-to-haves, and extras – against the technology’s technical role within the organization.
5. Weigh the implementation timeline against other options
Another factor to consider when reviewing digital-transformation technologies is the timeline for implementation. Timelines vary among the options to develop technology internally, merge existing or build technology with off-the-shelf packages, or solely implement off-the-shelf technology.
What, then, dictates what “completed” might look like? That would be the competitive environment and the return on investment for each possible course of action. And, because emerging technologies could have a negative impact on a longer-term implementation of a current deployment, another prudent option could be to wait things out until something more suited to the organization’s requirements comes along.
6. Weigh the fit within the current data ecosystem
Common sense suggests that systems requiring less capital outlay or time to implement should be given more weight in the selection analysis. Data-mature organizations, for example, typically have existing data platforms, so they don’t have to reinvent the wheel during a digital transformation. Other organizations must determine their plan for a data platform, and whether or not the selected technology will fit in.
In addition to considering the maturity level of the existing data ecosystem and what’s already in place, consider the industry in which the company exists and, again, the all-important corporate vision. Everything is relative, after all. Not every organization is going to try to achieve maximum transformation because it’s not an urgent industry need or because to do so might not be in its corporate strategy.
7. Weigh potential trade-offs
It bears repeating. Core functional requirements carry the most weight during an assessment of candidates in the technology-selection process; the type of data model that’s to be implemented also weighs heavily. In looking for the best ROI, sometimes a trade-off between the two criteria is valuable. Again, any compromise considered should be viewed through the lens of the corporate strategy and the end goals for acquiring the technology in the first place, not that of the application’s features, particularly those nice-to-have or “luxury” benefits.
8. Look for fit beyond the current scope
ROI can be hastened by leveraging data that new technology unearths and analyzes. On one level, that’s done by implementing the use of the data within other business lines or office locations. A $40,000 application, for example, isn’t as negatively impactful to the budget when it’s implemented across multiple offices. A second way to determine ROI is to monetize the extra bells and whistles the technology brings beyond the initially specified needs.
In the best-case scenario, the outcome is achieving a combination of extended internal use and capabilities beyond those anticipated. This is where global organizations still at an infancy level of data maturity grow up quickly, and the monetization of the technology is well worth the protracted exercise of selecting the best technology.
The Value of Digital Transformations
Digital transformations are becoming increasingly important to a company’s lifecycle. Start the transformation process by selecting technologies that primarily cater to the company’s overall corporate strategies. Beyond that, measure, with weighted deliberations, each potential selection’s ROI against the organization’s implementation timeline, existing capabilities, current data environment, and monetization potential. In the end, given the vast scope of possibilities, such a process might earn an organization benefits well beyond expectations.