data governance

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Startups must curb bureaucracy to ensure agile data governance

Published April 1, 2021

Jon Loyens
Contributor

Jon Loyens is chief product officer and co-founder of Data.World.

By now, all companies are fundamentally data driven. This is true regardless of whether they operate in the tech space. Therefore, it makes sense to examine the role data management plays in bolstering — and, for that matter, hampering — productivity and collaboration within organizations.

While the term “data management” inevitably conjures up mental images of vast server farms, the basic tenets predate the computer age. From censuses and elections to the dawn of banking, individuals and organizations have long grappled with the acquisition and analysis of data.

By understanding the needs of all stakeholders, organizations can start to figure out how to remove blockages.

One oft-quoted example is Florence Nightingale, a British nurse who, during the Crimean war, recorded and visualized patient records to highlight the dismal conditions in frontline hospitals. Over a century later, Nightingale is regarded not just as a humanitarian, but also as one of the world’s first data scientists.

As technology began to play a greater role, and the size of data sets began to swell, data management ultimately became codified in a number of formal roles, with names like “database analyst” and “chief data officer.” New challenges followed that formalization, particularly from the regulatory side of things, as legislators introduced tough new data protection rules — most notably the EU’s GDPR legislation.

This inevitably led many organizations to perceive data management as being akin to data governance, where responsibilities are centered around establishing controls and audit procedures, and things are viewed from a defensive lens.

That defensiveness is admittedly justified, particularly given the potential financial and reputational damages caused by data mismanagement and leakage. Nonetheless, there’s an element of myopia here, and being excessively cautious can prevent organizations from realizing the benefits of data-driven collaboration, particularly when it comes to software and product development.

Taking the offense

Data defensiveness manifests itself in bureaucracy. You start creating roles like “data steward” and “data custodian” to handle internal requests. A “governance council” sits above them, whose members issue diktats and establish operating procedures — while not actually working in the trenches. Before long, blockages emerge.

Blockages are never good for business. The first sign of trouble comes in the form of “data breadlines.” Employees seeking crucial data find themselves having to make their case to whoever is responsible. Time gets wasted.

By itself, this is catastrophic. But the cultural impact is much worse. People are natural problem-solvers. That’s doubly true for software engineers. So, they start figuring out how to circumvent established procedures, hoarding data in their own “silos.” Collaboration falters. Inconsistencies creep in as teams inevitably find themselves working from different versions of the same data set.

Microsoft acquires data privacy and governance service BlueTalon

Published July 29, 2019

Microsoft today announced that it has acquired BlueTalon, a data privacy and governance service that helps enterprises set policies for how their employees can access their data. The service then enforces those policies across most popular data environments and provides tools for auditing policies and access, too.

Neither Microsoft nor BlueTalon disclosed the financial details of the transaction. Ahead of today’s acquisition, BlueTalon had raised about $27.4 million, according to Crunchbase. Investors include Bloomberg Beta, Maverick Ventures, Signia Venture Partners and Stanford’s StartX fund.

“The IP and talent acquired through BlueTalon brings a unique expertise at the apex of big data, security and governance,” writes Rohan Kumar, Microsoft’s corporate VP for Azure Data. “This acquisition will enhance our ability to empower enterprises across industries to digitally transform while ensuring right use of data with centralized data governance at scale through Azure.”

Unsurprisingly, the BlueTalon team will become part of the Azure Data Governance group, where the team will work on enhancing Microsoft’s capabilities around data privacy and governance. Microsoft already offers access and governance control tools for Azure, of course. As virtually all businesses become more data-centric, though, the need for centralized access controls that work across systems is only going to increase and new data privacy laws aren’t making this process easier.

“As we began exploring partnership opportunities with various hyperscale cloud providers to better serve our customers, Microsoft deeply impressed us,” BlueTalon CEO Eric Tilenius, who has clearly read his share of “our incredible journey” blog posts, explains in today’s announcement. “The Azure Data team was uniquely thoughtful and visionary when it came to data governance. We found them to be the perfect fit for us in both mission and culture. So when Microsoft asked us to join forces, we jumped at the opportunity.”

As FTC cracks down, data ethics is now a strategic business weapon

Published July 12, 2019

Daniel Wu
Contributor

Dan Wu is a privacy counsel and legal engineer at Immuta. He holds a JD from Harvard University, and is a PhD candidate for Social Policy and Sociology at The Harvard Kennedy School.

Agile data governance helps startups obtain more data — and create more value

With agile data governance, startups can address their critical weakness: data scarcity. Customers share more data with startups that make data collection a feature, not a burdensome part of the user experience. Agile data governance systems simplify compliance with this data practice.

Take Ally Bank, which the Ponemon Institute rated as one of the most privacy-protecting banks. In 2017, Ally’s deposits base grew 16%, while those of incumbents declined 4%.

One key principle to its ethical data strategy: minimizing data collection and use. Ally’s customers obtain services through a personalized website, rarely filling out long surveys. When data is requested, it’s done in small doses on the site — and always results in immediate value, such as viewing transactions.

This is on purpose. Ally’s Chief Marketing Officer publicly calls the industry-mantra of “more data” dangerous to brands and consumers alike.

A critical tool to minimize data use is to use advanced data privacy tools like differential privacy. A favorite of organizations like Apple, differential privacy limits your data analysts’ access to summaries of data, such as averages. And by injecting noise into those summaries, differential privacy creates provable guarantees of privacy and prevents scenarios where malicious parties can reverse-engineer sensitive data. But because differential privacy uses summaries, instead of completely masking the data, companies can still draw meaning from it and improve their services.

With tools like differential privacy, organizations move beyond governance patterns where data analysts either gain unrestricted access to sensitive data (think: Uber’s controversial “god view”) or face multiple barriers to data access. Instead, startups can use differential privacy to share and pool data safely, helping them overcome data scarcity. The most agile data governance systems allow startups to use differential privacy without code and the large engineering teams that only incumbents can afford.

Ultimately, better data means better predictions — and happier customers.

Agile data governance cultivates customer loyalty

According to Deloitte, 80% of consumers are more loyal to companies they believe protect their data. Yet far fewer leaders at established, incumbent companies — the respondents of the same survey — believed this to be true. Customers care more about their data than the leaders at incumbent companies think.

This knowledge gap is an opportunity for startups.

Furthermore, big enterprise companies — themselves customers of many startups — say data compliance risks prevent them from working with startups. And rightly so. Over 80% of data incidents are actually caused by errors from insiders, like third party vendors who mishandle sensitive data by sharing it with inappropriate parties. Yet over 68% of companies do not have good systems to prevent these types of errors. In fact, Facebook’s Cambridge Analytica firestorm — and resulting $5 billion fine — was sparked by third party inappropriately sharing personal data with a political consulting firm without user consent.

As a result, many companies — both startups and incumbents — are holding a ticking time bomb of customer attrition.

Agile data governance defuses these risks by simplifying the ethical data practices of understanding, controlling, and monitoring data at all times. With such practices, startups can prevent and correct the mishandling of sensitive data quickly.

Cognoa is a good example of a Third Wave healthcare startup adopting these three practices at a rapid pace. First, it understands where all of its sensitive health data lies by connecting all of its databases. Second, Cognoa can control all connected data sources at once from one point by using a single access-and-control layer, as opposed to relying on data silos. When this happens, employees and third parties can only access and share the sensitive data sources they’re supposed to. Finally, data queries are always monitored, allowing Cognoa to produce audit reports frequently and catch problems before they escalate out of control.

With tools that simplify these three practices, even low-resourced startups can make sure sensitive data is tightly controlled at all times to prevent data incidents. Because key workflows are simplified, these same startups can maintain the speed of their data analytics by sharing data safely with the right parties. With better and safer data sharing across functions, startups can develop the insight necessary to cultivate a loyal fan base for the long-term.

Agile data governance can help startups survive inevitable data incidents

In 2018, Panera mistakenly shared 37 million customer records on its website and took 8 months to respond. Panera’s data incident is a taste of what’s to come: Gartner predicts that 50% of business ethics violations will stem from data incidents like these. In the era of “Big Data,” billion dollar incumbents without agile data governance will likely continue to violate data ethics.

Given the inevitability of such incidents, startups that adopt agile data governance will likely be the most resilient companies of the future.

Case in point: Harvard Business Review reports that the stock prices of companies without strong data governance practices drop 150% more than companies that do adopt strong practices. Despite this difference, only 10% of Fortune 500 companies actually employ the data transparency principle identified in the report. Practices include clearly disclosing data practices and giving users control over their privacy settings.

Sure, data incidents are becoming more common. But that doesn’t mean startups don’t suffer from them. In fact, up to 60% of startups fold after a cyber attack.

Startups can learn from WebMD, which Deloitte named as one standout in applying data transparency. With a readable privacy policy, customers know how data will be used, helping customers feel comfortable about sharing their data. More informed about the company’s practices, customers are surprised less by incidents. Surprises, BCG found, can reduce consumer spending by one-third. On a self-service platform on WebMD’s site, customers can control their privacy settings and how to share their data, further cultivating trust.

Self-service tools like WebMD’s are part of agile data governance. These tools allow startups to simplify manual processes, like responding to customer requests to control their data. Instead, startups can focus on safely delivering value to their customers.

Get ahead of the curve

For so long, the public seemed to care less about their data.

That’s changing. Senior executives at major companies have been publicly interrogated for not taking data governance seriously. Some, like Facebook and Apple, are even claiming to lead with privacy. Ultimately, data privacy risks significantly rise in Third Wave industries where errors can alter access to key basic needs, such as healthcare, housing, and transportation.

While many incumbents have well-resourced legal and compliance departments, agile data governance goes beyond the “risk mitigation” missions of those functions. Agile governance means that time-consuming and error-prone workflows are streamlined so that companies serve their customers more quickly and safely.

Case in point: even after being advised by an army of lawyers, Zuckerberg’s 30,000-word Senate testimony about Cambridge Analytica included “ethics” only once, and it excluded “data governance” completely.

And even if companies do have legal departments, most don’t make their commitment to governance clear. Less than 15% of consumers say they know which companies protect their data the best. Startups can take advantage of this knowledge gap by adopting agile data governance and educate their customers about how to protect themselves in the risky world of the Third Wave.

Some incumbents may always be safe. But those in highly-regulated Third Wave industries, such as automotive, healthcare, and telecom should be worried; customers trust these incumbents the least. Startups that adopt agile data governance, however, will be trusted the most, and the time to act is now.

How to handle dark data compliance risk at your company

Published April 2, 2019

Lisa Hawke
Contributor

Lisa Hawke is VP of Security and Compliance at Everlaw, and Vice Chair of Women in Security and Privacy.

Slack and other consumer-grade productivity tools have been taking off in workplaces large and small — and data governance hasn’t caught up.

Whether it’s litigation, compliance with regulations like GDPR or concerns about data breaches, legal teams need to account for new types of employee communication. And that’s hard when work is happening across the latest messaging apps and SaaS products, which make data searchability and accessibility more complex.

Here’s a quick look at the problem, followed by our suggestions for best practices at your company.

Problems

The increasing frequency of reported data breaches and expanding jurisdiction of new privacy laws are prompting conversations about dark data and risks at companies of all sizes, even small startups. Data risk discussions necessarily include the risk of a data breach, as well as preservation of data. Just two weeks ago it was reported that Jared Kushner used WhatsApp for official communications and screenshots of those messages for preservation, which commentators say complies with record keeping laws but raises questions about potential admissibility as evidence.

The economics and tradeoffs of ad-funded smart city tech

Published December 1, 2018

In order to have innovative smart city applications, cities first need to build out the connected infrastructure, which can be a costly, lengthy, and politicized process. Third-parties are helping build infrastructure at no cost to cities by paying for projects entirely through advertising placements on the new equipment. I try to dig into the economics of ad-funded smart city projects to better understand what types of infrastructure can be built under an ad-funded model, the benefits the strategy provides to cities, and the non-obvious costs cities have to consider.

Consider this an ongoing discussion about Urban Tech, its intersection with regulation, issues of public service, and other complexities that people have full PHDs on. I’m just a bitter, born-and-bred New Yorker trying to figure out why I’ve been stuck in between subway stops for the last 15 minutes, so please reach out with your take on any of these thoughts: @Arman.Tabatabai@techcrunch.com.

Using ads to fund smart city infrastructure at no cost to cities

When we talk about “Smart Cities”, we tend to focus on these long-term utopian visions of perfectly clean, efficient, IoT-connected cities that adjust to our environment, our movements, and our every desire. Anyone who spent hours waiting for transit the last time the weather turned south can tell you that we’ve got a long way to go.

But before cities can have the snazzy applications that do things like adjust infrastructure based on real-time conditions, cities first need to build out the platform and technology-base that applications can be built on, as McKinsey’s Global Institute explained in an in-depth report released earlier this summer. This means building out the network of sensors, connected devices and infrastructure needed to track city data.

However, reaching the technological base needed for data gathering and smart communication means building out hard physical infrastructure, which can cost cities a ton and can take forever when dealing with politics and government processes.

Many cities are also dealing with well-documented infrastructure crises. And with limited budgets, local governments need to spend public funds on important things like roads, schools, healthcare and nonsensical sports stadiums which are pretty much never profitable for cities (I’m a huge fan of baseball but I’m not a fan of how we fund stadiums here in the states).

As city infrastructure has become increasingly tech-enabled and digitized, an interesting financing solution has opened up in which smart city infrastructure projects are built by third-parties at no cost to the city and are instead paid for entirely through digital advertising placed on the new infrastructure.

I know – the idea of a city built on ad-revenue brings back soul-sucking Orwellian images of corporate overlords and logo-paved streets straight out of Blade Runner or Wall-E. Luckily for us, based on our discussions with developers of ad-funded smart city projects, it seems clear that the economics of an ad-funded model only really work for certain types of hard infrastructure with specific attributes – meaning we may be spared from fire hydrants brought to us by Mountain Dew.

While many factors influence the viability of a project, smart infrastructure projects seem to need two attributes in particular for an ad-funded model to make sense. First, the infrastructure has to be something that citizens will engage – and engage a lot – with. You can’t throw a screen onto any object and expect that people will interact with it for more than 3 seconds or that brands will be willing to pay to throw their taglines on it. The infrastructure has to support effective advertising.

Second, the investment has to be cost-effective, meaning the infrastructure can only cost so much. A third-party that’s willing to build the infrastructure has to believe they have a realistic chance of generating enough ad-revenue to cover the costs of the projects, and likely an amount above that which could lead to a reasonable return. For example, it seems unlikely you’d find someone willing to build a new bridge, front all the costs, and try to fund it through ad-revenue.

When is ad-funding feasible? A case study on kiosks and LinkNYC

A LinkNYC kiosk enabling access to the internet in New York on Saturday, February 20, 2016. Over 7500 kiosks are to be installed replacing stand alone pay phone kiosks providing free wi-fi, internet access via a touch screen, phone charging and free phone calls. The system is to be supported by advertising running on the sides of the kiosks. ( Richard B. Levine) (Photo by Richard Levine/Corbis via Getty Images)

To get a better understanding of the types of smart city hardware that might actually make sense for an ad-funded model, we can look at the engagement levels and cost structures of smart kiosks, and in particular, the LinkNYC project. Smart kiosks – which provide free WiFi, connectivity and real-time services to citizens – have been leading examples of ad-funded smart city projects. Innovative companies like Intersection (developers of the LinkNYC project), SmartLink, IKE, Soofa, and others have been helping cities build out kiosk networks at little-to-no cost to local governments.

LinkNYC provides public access to much of its data on the New York City Open-Data website. Using some back-of-the-envelope math and a hefty number of assumptions, we can try to get to a very rough range of where cost and engagement metrics generally have to fall for an ad-funded model to make sense.

To try and retrace considerations for the developers’ investment decision, let’s first look at the terms of the deal signed with New York back in 2014. The agreement called for a 12-year franchise period, during which at least 7,500 Link kiosks would be deployed across the city in the first eight years at an expected project cost of more than $200 million. As part of its solicitation, the city also required the developers to pay the greater of either a minimum annual payment of at least $17.5 million or 50 percent of gross revenues.

Let’s start with the cost side – based on an estimated project cost of around $200 million for at least 7,500 Links, we can get to an estimated cost per unit of $25,000 – $30,000. It’s important to note that this only accounts for the install costs, as we don’t have data around the other cost buckets that the developers would also be on the hook for, such as maintenance, utility and financing costs.

Source: LinkNYC, NYC.gov, NYCOpenData

Turning to engagement and ad-revenue – let’s assume that the developers signed the deal with the expectations that they could at least breakeven – covering the install costs of the project and minimum payments to the city. And for simplicity, let’s assume that the 7,500 links were going to be deployed at a steady pace of 937-938 units per year (though in actuality the install cadence has been different). In order for the project to breakeven over the 12-year deal period, developers would have to believe each kiosk could generate around $6,400 in annual ad-revenue (undiscounted).

Source: LinkNYC, NYC.gov, NYCOpenData

The reason the kiosks can generate this revenue (and in reality a lot more) is because they have significant engagement from users. There are currently around 1,750 Links currently deployed across New York. As of November 18th, LinkNYC had over 720,000 weekly subscribers or around 410 weekly subscribers per Link. The kiosks also saw an average of 18 million sessions per week, or 20-25 weekly sessions per subscriber, or around 10,200 weekly sessions per kiosk (seasonality might even make this estimate too low).

And when citizens do use the kiosks, they use it for a long time! The average session for each Link unit was four minutes and six seconds. The level of engagement makes sense since city-dwellers use these kiosks in time or attention-intensive ways, such making phone calls, getting directions, finding information about the city, or charging their phones.

The analysis here isn’t perfect, but now we at least have a (very) rough idea of how much smart kiosks cost, how much engagement they see, and the amount of ad-revenue developers would have to believe they could realize at each unit in order to ultimately move forward with deployment. We can use these metrics to help identify what types of infrastructure have similar profiles and where an ad-funded project may make sense.

Bus stations, for example, may cost about $10,000 – $15,000, which is in a similar cost range as smart kiosks. According to the MTA, the NYC bus system sees over 11.2 million riders per week or nearly 700 riders per station per week. Rider wait times can often be five-to-ten minutes in length if not longer. Not to mention bus stations already have experience utilizing advertising to a certain degree. Projects like bike-share docking stations and EV charging stations also seem to fit similar cost profiles while having high engagement.

And interactions with these types of infrastructure are ones where users may be more receptive to ads, such as an EV charging station where someone is both physically engaging with the equipment and idly looking to kill up sometimes up to 30 minutes of time as they charge up. As a result, more companies are using advertising models to fund projects that fit this mold, like Volta, who uses advertising to offer charging stations free to citizens.

The benefits of ad-funding come with tradeoffs for cities

When it makes sense for cities and third-party developers, advertising-funded smart city infrastructure projects can unlock a tremendous amount of value for a city. The benefits are clear – cities pay nothing, citizens are offered free connectivity and real-time information on local conditions, and smart infrastructure is built and can possibly be used for other smart city applications down the road, such as using locational data tracking to improve city zoning and congestion.

Yes, ads are usually annoying – but maybe understanding that advertising models only work for specific types of smart city projects may help quell fears that future cities will be covered inch-to-inch in mascots. And ads on projects like LinkNYC promote local businesses and can tap into idiosyncratic conditions and preferences of regional communities – LinkNYC previously used real-time local transit data to display beer ads to subway riders that were facing heavy delays and were probably in need of a drink.

Like everyone’s family photos from Thanksgiving, the picture here is not all roses, however, and there are a lot of deep-rooted issues that exist under the surface. Third-party developed, advertising-funded infrastructure comes with externalities and less obvious costs that have been fairly criticized and debated at length.

When infrastructure funding is derived from advertising, concerns arise over whether services will be provided equitably across communities. Many fear that low-income or less-trafficked communities that generate less advertising demand could end up having poor infrastructure and maintenance.

Even bigger points of contention as of late have been issues around data consent and treatment. I won’t go into much detail on the issue since it’s incredibly complex and warrants its own lengthy dissertation (and many have already been written).

But some of the major uncertainties and questions cities are trying to answer include: If third-parties pay for, manage and operate smart city projects, who should own data on citizens’ living behavior? How will citizens give consent to provide data when tracking systems are built into the environment around them? How can the data be used? How granular can the data get? How can we assure citizens’ information is secure, especially given the spotty track records some of the major backers of smart city projects have when it comes to keeping our data safe?

The issue of data treatment is one that no one has really figured out yet and many developers are doing their best to work with cities and users to find a reasonable solution. For example, LinkNYC is currently limited by the city in the types of data they can collect. Outside of email addresses, LinkNYC doesn’t ask for or collect personal information and doesn’t sell or share personal data without a court order. The project owners also make much of its collected data publicly accessible online and through annually published transparency reports. As Intersection has deployed similar smart kiosks across new cities, the company has been willing to work through slower launches and pilot programs to create more comfortable policies for local governments.

But consequential decisions related to third-party owned smart infrastructure are only going to become more frequent as cities become increasingly digitized and connected. By having third-parties pay for projects through advertising revenue or otherwise, city budgets can be focused on other vital public services while still building the efficient, adaptive and innovative infrastructure that can help solve some of the largest problems facing civil society. But if that means giving up full control of city infrastructure and information, cities and citizens have to consider whether the benefits are worth the tradeoffs that could come with them. There is a clear price to pay here, even when someone else is footing the bill.

And lastly, some reading while in transit:

How California’s Efforts to Prevent Wildfires Reflect a National Crisis on Climate Change – The New Yorker, Carolyn Kormann
A’s Propose Crazy-Looking Howard Terminal Stadium, Financial Details Still Uncertain – Field of Schemes, Neil deMause
Berlin’s Massive Housing Push Sparks a Debate About the City’s Future – CityLab, Feargus O’Sullivan
Magnetic Levitation: The Return of Transport’s Great ‘What If?’ – The Guardian, Christopher Beanland
Addis Ababa Has Overtaken Dubai as the World’s Gateway into Africa – Quartz, Abdi Latif Dahir
Planning a Neighborhood Square – The Western Planner, Dr. Suzanne H. Crowhurst Lennard

Clairvoyant launches Kogni to help companies track their most sensitive data

Published January 23, 2018

As we inch ever closer to GDPR in May, companies doing business in Europe need to start getting a grip on the sensitive private data they have. The trouble is that as companies move their data into data lakes, massive big data stores, it becomes more difficult to find data in a particular category. Clairvoyant, an Arizona company is releasing a tool called Kogni that could help.
Chandra… Read More

Collibra snags $58M Series D led by Iconiq and Battery Ventures to simplify data governance

Published January 8, 2018

Collibra, a company that wants to help firms understand data governance, announced a $58 million Series D funding round today led by Iconiq Capital and Battery Ventures.
All of the investors involved in this round were coming back for another dip in the well. In addition to Iconiq and Battery Ventures, early Collibra investors Dawn Capital, Index Ventures and Newion Investments also participated. Read More

Collibra nabs $50M at reported $650M valuation led by ICONIQ

Published January 18, 2017

big data words Data governance and management startup Collibra — originally founded in Belgium but now based out of New York to help businesses in sectors like finance and healthcare to manage and comply with data retention policies — has raised $50 million in its latest round of funding. The company is not disclosing the valuation, but we heard that it is in the region of $650 million (which is… Read More