Personal data is the raw material that fuels a significant proportion of business operations. A few examples include credit card scoring based on collated personal data from various sources, calculation of premiums based on past driving habits, or the use of online tracking to build complete profiles of individuals and then targeting them with personalized ads based on those profiles. While personal data is highly essential to these business operations, individuals have little to no control and oversight on the collection and usage of their personal data.

There is anger towards the data economy and frequent privacy violations; there are still ways to restore control to the people and rebuild a trust-based and transparent relationship.

This lack of control is due to a few practices common to the current data collection and usage practices:

• Personal data is scattered across so many different companies that it is nearly impossible to keep track of who accesses it, how they use it or who they share it with. For example, data brokers’ business model depends on the collecting, collating, selling and licensing personal data on a mass scale. It is next to impossible to track data across systems and determine whether the data was obtained lawfully or object to the processing of data.
• The reproducible nature of data exacerbates the risks even further, contributing to a growing fear over privacy. Once personal data enters into a business’ internal systems, it can be copied to multiple locations, used by employees on their personal devices, left unprotected on legacy servers. All these processing activities increase unauthorized use or access to personal data.
• Collection, analysis, and personal data transfer are usually conducted behind closed doors not visible to individuals and often with technologies such as machine learning, which is opaque to ordinary individuals. Individuals are often not adequately informed about the use of their data due to reasons such as trade secrets, impracticality, or simply the bureaucratic hurdles caused by the relevant business itself. Even laws such as GDPR and CCPA may not be effective at coercing a business to provide the maximum transparency possible.

Individuals’ lack of knowledge on collection, use and sharing of their personal data inevitably leads to distrust in companies involved in personal data collection.

The imbalance of power and lack of trust is evidenced by a PRC study that found that 76% of Americans do not trust third-party businesses to handle their personal data and feel a sense of lack of control over how their data is collected, managed and used.

Furthermore, Americans outside of California want to have more control over their data and want to have the same protections on their personal data as regulated under CCPA (91%).

While consumer demands are crystal-clear, how to deliver on those demands remains unclear. Personal Data Stores, however, can be an effective solution to remedy consumer concerns and provide them the visibility and control over their data.

Personal Data Stores – An unconventional solution to a bleeding problem

What is the Personal Data Store?

Personal Data Store (PDS) is like a safe for individuals to upload, share, store, edit and erase their personal information, such as addresses, passport numbers, credit history, health records and other information.

One unique character of the PDS is that users(consumers) can unilaterally grant or withdraw consent to access their personal data. Once the consumer decides to block access to her data, the relevant business is prevented from accessing it.

How Personal Data Stores help consumers regain control over their data?

1. Increased transparency equals stronger control

Firstly, Personal Data Store gives complete visibility over what data an individual has, who accesses it, how it is used, and for what purposes.

The scattered nature of personal data in the current ecosystem makes it impossible for individuals to track who retains their data and who they share it with. For example, home address data could be captured and stored by data brokers, postal offices, e-commerce companies and various other entities. If individual wishes to find out who uses their data and how, it would be challenging to contact each entity, fill out forms, and then track requests.

With Personal Data Stores, however, individuals are given exclusive control and visibility over how their data is processed and by whom. Increased transparency is a prerequisite to having control over data and this is what personal data stores achieve.

Thanks to this visibility, consumers can withdraw access to certain third parties, edit personal data that is not accurate and ask for the deletion of their data.

2. Stronger control enables the exercise of privacy rights under the relevant laws

New privacy laws such as GDPR and CCPA provided new rights to consumers, such as the right to deletion of their data, the right to rectify inaccurate data and the right to restrict access to their data.

For consumers to properly exercise their rights under these laws, they first must have complete information about the collection and use of their data. Exercising privacy rights is a decision, and this decision will not be well-informed without individuals having control and visibility.

Via Personal Data Stores, individuals can see which specific data is accessed by which specific third-party on a granular level.

One factor that plays a vital role in the successful implementation of privacy rights is a convenient and swift exercise of those rights. If a consumer has to fill out tens of details to complete a form, wait for weeks to get her privacy right fulfilled, then the essence of such privacy rights would be undermined because the consumers would be discouraged from using their rights.

What if a person changes her health insurance plan and now has to contact multiple pharmacies and hospitals to update this detail?

New privacy laws exist to restore control to the individuals, and this cannot be achieved with processes that make it unbearable for individuals even to try to exercise their rights. In other words, the individuals would not be empowered but rather find themselves in the same powerless position.

Personal Data Store serves the purpose of privacy laws because it streamlines the process of exercising privacy rights such as deletion and data rectification rights. It provides a single user-interface that people can use to send their requests without dealing with the separate and cumbersome procedures set by third-party businesses.

Suppose an individual wishes data concerning her unsuccessful job applications deleted, for instance. In that case, she can log this request via the Personal Data Store, and all relevant third parties will be notified of this request and they will have to execute on such request.

A better future for privacy lies ahead.

New privacy regulations across the globe brought significant obligations on businesses to respect privacy and allow individuals to exercise certain rights over their data. While these new laws and the expansion of privacy is to be celebrated, there is still more work to be done. Personal Data Store can contribute to individuals’ empowerment by allowing them to exercise stricter control over the access and usage of their data.

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Teams are drowning in data. The ease of collecting data has led to popularizing ideas like big data, data warehouses, and machine learning. The problem is that companies can get stuck trying sort through their data.

In this post, I want to share 3 strategies for helping you solve this problem. The goal isn’t to see how much data you could collect. The goal is to uncover insights about your customers, your products, and your business.

Does Your Team Know Exactly What Data You Have?

The MoMA or Museum of Modern Art has a peculiar problem. They have one of the most extensive Persian rug collections globally, but they aren’t sure what’s in the collection. The curator has never seen most of the rugs that he has purchased.

This is like the adage of the tree falling in the forest. If no one is there to watch or hear the tree fall, did it really happen?

Companies find themselves in a similar situation with their data. They have a lot of data, but no one is quite sure what is available. They have never seen some of the data they collected and could be hypothetical for all they know.

There’s no point in keeping data hidden. Data doesn’t become valuable until it is converted into insights. Before that point, it is simply information or computer bytes if we are technical. Executives, managers, and employees want more insights out of their data, not more data.

The first strategy for sorting through your data is to know exactly what is there. This can be done through an audit of everything that is being collected and store. The process sounds intimidating, and it can be for larger companies. You’ll need to hunt down obscure documents, check unused products, and scramble to find logins for software tools that no one ever uses.

Once you know what’s in your data vault, you need to tell people about it. This is commonly called “data literacy.” It simply means that everyone in your company understands what data is being collected and how they could use it.

If they wanted to check on the latest purchases, they know where to go for that data and how to visualize. Whether this happens through SQL, Tableau, Power BI, or even in Excel, that’s beside the point.

Like education, the more you have of it, the more useful it is. If you have poor data literacy, it’s like being surrounded by books you can’t read. They are merely random characters on a page.

Trust Issues Can Be Detrimental

Have you ever had someone say, “I don’t trust this number?” I call this Funky Data.

When you look at a report or dashboard, you can’t seem to trust the numbers in front of you. You may not be sure why these numbers don’t make sense, but there’s something weird (or funky) about them.

Ensuring that your team has trust in your data is the next strategy in our repertoire. You may have the best data in the world, and your team knows this, but if they don’t trust it, they won’t use it.

Lack of trust is one of the most pervasive issues that I help companies work through. It starts small, but it can grow to become a monster over time. At its worst, people cannot trust anything they see in terms of numbers, so they rely on opinions and anecdotes.

There are 3 Funky Data scenarios that you need to be aware of:

1. Technical Issues: this is what people typically think when they see a number they can’t trust. There’s a technical issue in how the data was collected or visualized. You solve this by debugging the report and deconstructing how a number came to be.

2. Misaligned Expectations: in this scenario, people expected what a number must be, but the actual data doesn’t support this. This is common for teams who haven’t had much data and were operating on opinions for most of the past. You solve this by working through the expectations and showing that the actual data is correct.

3. Unexpected Calculations: this is when a calculation doesn’t show you what you expect. You may be expecting to see 1000 purchases, but the report only shows 800. However, the calculation limits the data to only new customer purchases, but you don’t know this. You can solve this by working through the calculations and showing the assumptions that are taken into consideration.

Scenarios 2 and 3 are mostly psychological. These make them the hardest problems to solve. They require empathy and patience.

You Can’t Quench Your Thirst with a Broken Fire Hydrant.

Our third strategy will deal with overwhelm by having too much data available to you. This is what everyone feels when they open Google Analytics for the first time. There’s so much data available to you on one screen that you aren’t sure where to even begin.

This is like trying to quench your thirst from a broken fire hydrant. The force of the water would be too much, and you would be tired from even just attempting it.

After your team knows what data you’re collecting and solve any trust issues, you need to make data easily digestible. Remember that the goal is insights, so we don’t get brownie points for the data volume that we collect.

Here are a few ideas to reducing data overwhelm:

• Create a unique dashboard for every team and individual. This allows you to limit how much data is available at any given time.
• Be opinionated on how data should be visualized but offer options for people to filter and slice the data.
• Convert the data into multiple formats: dashboard, email digests, SMS, CSV exports, and others.
• Use machine learning to discover patterns within data sets.

Conclusion

Data can be a goldmine, but you need the right equipment and approach; otherwise, you’ll just be digging through the mud. Start by helping your team understand exactly what data is available, tackle any pervasive trust issues, and implement different ways to reduce data overwhelm.

The post 3 Overlooked Strategies for Getting More Insights Out of Your Data appeared first on ReadWrite.