IoT and data governance – is more necessarily better?
The development of smart devices, coupled with the incessant need to collect data for all and any reason, has contributed to a new challenge for organizations in the field of manufacturing of these types of devices in general, and analytics in particular.
The challenge of big data is that it has turned into “too much data.” And this is not only in the IoT arena, but in any industry where organizations collect data for revenue purposes. Cisco shows in their survey around data collection from IoT that 5 quintillion bytes of data are currently being produced daily by IoT devices, yet only 26 percent of organizations reported success on their IoT initiatives.
Organizations have realized that data is a strategic asset and a lot of them are trying to commoditize it. In the case of IoT, not all data is created equal. Simply hoarding data because it may be useful one day may create a much higher risk than making decisions about data that make sense for a specific organization.
In the case of IoT, this has become a huge challenge because smart devices can gather unimaginable amounts of data. However, the fact that they can doesn’t mean that they should.
I will not get into the details of risks around cybersecurity because that has been debated ad nauseam. I am interested in discussing the other side of the coin: business opportunities. What does having a clear strategy for the collection and use of data gathered from IoT devices mean in terms of revenue and profitability? How can data governance help achieve that goal?
The data governance approach: turning data into meaningful insights
Data governance is the framework under which data is managed within an organization to ensure the appropriate collection (the “what to use”), processing (the “how to use”), retention (the “until when to use”) and relevance (the “why to use”) of data. At the same time, data governance is ensuring that integrity, security and compliance guidelines are in place to maximize business objectives.
Data governance, therefore, dictates the boundaries under which data will be utilized to the advantage of the organization. Without this structure, any team would be lost in a sea of data without being able to turn it into comprehensive knowledge and insights.
Data governance is the first step to building a clear competitive advantage that has perhaps been lost because organizations are focused on speed and volume, not true value. As Harvard Business Review found out, less than half of structured data is used on decision-making. This indicates a clear disconnect between the amount of data we are collecting and how we are using it for the advantage of the business.
Which brings me back to my initial premise: is more necessarily better? The answer is no. We have come to the point where we need to focus on quality and forget about quantity.
Lack of a clear strategy around data and inability to govern how that data is used for the purpose of such strategy, is creating issues that companies cannot overcome. They are spending more on marketing campaigns not tailored to their audience; they are duplicating efforts with the amount of administration they are investing in garnering insights from data; they are increasing their risks from security and a privacy perspective; and they cannot make sense of what they have and what it can give them.
We are at a crossroads and it seems that even regulators are stepping in to calm the chaos. The General Data Protection Regulation (GDPR) in Europe is setting strict rules about how personal data should be managed and California with the California Consumer Privacy Act (CCPA) is following suit.
However, if we look at these regulations closer, the principles of GDPR or CCPA can be used as an overall data (personal and business) governance framework. GDPR and CCPA are setting the stage to ensure that data is collected, processed and retained for the right purpose. Why can’t we do the same for all data whether it is personal or not?
For IoT, we are in urgent need to set the right rules and boundaries to ensure that the data that is produced by these devices can provide value – for the consumer and therefore for the organization – to make our lives easier.