Data Validation: The Opportunity for the Channel
Data is generated by everything – from buildings and machinery to employees and customers. Each of these data sources has the power to change the relative success of a company, though only if data is understood, accurate, analyzed and acted upon.
The result for the channel? Plenty of enterprises are unsure which data are appropriate to meet their business objectives. Fortunately for channel players, this complexity equals new sales opportunities. An ideal proposition is one that questions the quality of a company’s data.
Remember, for senior decision-makers, validated data is the only type they need to do their jobs.
Why Doesn’t Every VAR Offer This Solution?
Well, the most significant challenge is market education.
Most enterprises have never invested in a data validation solution, therefore budget holders have little experience in understanding what the business value of a data audit is.
In fact, many organizations are only prioritizing analytics projects because they feel they must. They fear being left behind by competitors already investing heavily in digital transformation.
Another reason is executives simply presume reports are correct. The reality is different. Inaccurate data generate reports that do not reflect business truth.
How and why is this happening? Sensors and data collection methods are prone to errors just like any other type of technology. Sensors can produce incorrect or unintelligible data, report sporadically and in some cases, record no data at all. It can take weeks, months or even longer for someone to notice inaccuracies.
As the Internet of Things matures and an increasing number of data collection points are embedded within a business’s workflows, preventing this scenario will become even more important. Expect to see an increasing number of end-users proactively searching for solutions that can improve the quality of their data.
Finding Time to Validate
While this demand creates an ideal sales scenario, there is still the question of how to commercialize a solution such as data validation?
Once you’ve built partnerships with companies offering certification programs and solutions, start identifying clients already using analytics.
Ask stakeholders these questions:
- Is your solution calibrated correctly? How do you know if it has been?
- Are your data collection methods certified as ‘clean’? Where’s the proof?
- Is ‘bad data’ cleansed from the system? With what method?
- Are report results too good to be true?
If these questions cannot be confidently answered, there’s your sales opportunity.
A Prime Candidate
Still need convincing why you should add data validation to your sales cycle?
Take the data center as an example. In its most basic form, data centers are large buildings housing expensive IT equipment and complex infrastructure, however every component enables employees to do their jobs. No data center, no business.
The result for enterprises is an expensive facility and a corporate asset with the potential to drain a company’s finances dramatically.
Any data center built on inaccurate financial and operational data can have a catastrophic effect on customer relationships and a company’s sustainability profile. If a miscalculation occurs, it can lead to service issues or worse, full facility failure.
A Sub-Sector of Analytics
As expected, a whole raft of analytics solutions has emerged to counter these risks. They offer businesses and data center operators transparency in their facilities. Some provide operational visibility, other analytics applications are used for strategic planning, scientific analysis and engineering.
The element that unites every solution in this sector is a reliance on data. Data that must be precise but in many cases isn’t. A large proportion of data centers still have malfunctioning sensors, missing data and temperamental metering, yet managers are not aware of issues because nothing has ever been verified.
Accurate data enables an enterprise to clearly assess the state of its data centers and what improvements are needed to drive efficiency, manage capital investment and reduce environmental impact.
The above example covers a single business unit, but it’s a crucial one.
So, if you’re considering starting with the data center, follow the below action-plan:
- Segment which clients own data centers and which use the cloud.
- Understand the analytics solutions being used.
- Look into prospective data validation and audit providers to partner with.
- Uncover each client or prospect’s objectives – financial, efficiency programs, CSR.
- Sell, sell, sell!
Service your client base first. It should create sustainable revenue over time. After all, data will continue growing in scale and need regular auditing.
While recurring revenue streams are being established, look outwardly for new opportunities. Targets worth considering are companies investing in facilities, energy management and critical infrastructure automation. These are becoming more of a competitive advantage, source for financial improvement and vendor selection criteria. They need accurate data.
Your ultimate goal should be to become a trusted channel partner, an expert in delivering as much value as possible from a company’s analytics solutions and the data they own. One way to deliver on this objective is to push vendors to guide their partners and customers in data gathering practices and the analysis of data.
It’s a joint effort, after all. So, the next time you are approached by a prospective client on how to deliver a successful analytics application project, start with the most important aspect – data.
Richard Jenkins, SVP of Global Marketing & Strategic Partnerships, Romonet
Internationally-focused sales and marketing professional in enterprise software, data center analytics, IoT, cleantech and multimedia. On leaving the Royal Navy in 1990, Richard worked for UK-based IT resellers developing channels across EMEA, building and selling a Y2K IT contractor firm, and providing IT consulting services to London-based banks. Other positions include Tivoli/IBM and Crystal Decisions (later Business Objects), Corporate Radar, Kyoto Planet, Plantiga and RF Code.