Data Governance
Blockchain, Data Governance, and Smart Contracts in a Post-COVID-19 World
The last few months have been very disruptive to nearly everyone across the globe. There are business challenges galore, such as managing large remote workforces – many of whom are new to working remotely and managing risk while attempting to conduct “business as usual.” Unfortunately, most businesses’ systems, processes, and internal controls were not designed for this “new normal.”
While there have been many predictions around Blockchain for the past few years, it is still not widely adopted. We are beginning to see an uptick in adopting Supply Chain Management Systems for reasons that include traceability of items – especially food and drugs. However, large-scale adoption has been elusive to date.
I believe we will soon see major shifts in mindset, investment, and effort toward modern digital technology driven by Data Governance and Risk Management. I also believe that this will lead to these technologies becoming easier to use via new platforms and integration tools, which will lead to faster adoption by SMBs and other non-enterprise organizations, and that will lead to the greater need for DevOps, Monitoring, and Automation solutions as a way to maintain control of a more agile environment.
Here are a few predictions:
- New wearable technology supporting Medical IoT will be developed to help provide an early warning system for disease and future pandemics. That will fuel innovations across industries, including Biotech and Pharma.
- Blockchain can provide data privacy, ownership, and provenance to ensure the data’s veracity.
- New legislation will be created to protect medical providers and other users of that data from being held liable for missing information or trends that could have saved lives or avoided other negative outcomes.
- In the meantime, Hospitals, Insurance Providers, and others will do everything possible to mitigate the risk of using Medical IoT data, which could include Smart Contracts to ensure compliance (assuming a benefit is provided to the data providers).
- Platforms may be created to offer individuals control over their own data, how it is used and by whom, ownership of that data, and payment for the use of that data. I wrote about this in 2013.
- Data Governance will be taken more seriously by every business. Today, companies talk about Data Privacy, Data Security, or Data Consistency, but few have a strategic end-to-end systematic approach to managing and protecting their data and their company.
- Comprehensive Data Governance will become a driving and gating force as organizations modernize and grow. Even before the pandemic, there were growing needs due to new data privacy laws and concerns around areas such as the data used for Machine Learning.
- In a business environment where more systems are distributed, the risk of data breaches and Cybercrime Increases. That must be addressed as a foundational component of any new system or platform.
- One or two Data Integration Companies will emerge as undisputed industry leaders because of their capabilities in MDM, Data Provenance and Traceability, and Data Access (an area typically managed by application systems).
- New standardized APIs akin to HL7 FHIR will be created to support a variety of industries as well as interoperability between systems and industries. Frictionless integration of key systems becomes even more important than it is today.
- Anything that can be maintained and managed in a secure and flexible distributed digital environment will be implemented to allow companies to quickly pivot and adapt to new challenges and opportunities on a global scale.
- Smart Contracts and Digital Currency Payment Processing Systems will likely be core components of those systems.
- This will also foster the growth of next-generation Business Ecosystems and more dynamic collaborations.
- Ongoing compliance monitoring, internal and external, will likely become a priority (“trust but verify”).
All in all, this is exciting from a business and technology perspective. Most companies must review and adjust their strategies and tactics to embrace these concepts and adapt to the coming New Normal.
The steps we take today will shape what we see and do in the coming decade, so it is important to get this right quickly, knowing that whatever is implemented today will live, evolve, and hopefully improve over time. Don’t wait for perfection, as the risks are too high.
The Unsung Hero of Big Data
Earlier this week, I read a blog post regarding the recent Gartner Hype Cycle for Advanced Analytics and Data Science, 2015. The Gartner chart reminded me of the epigram, “Plus ça change, plus c’est la même chose” (asserting that history repeats itself by stating the more things change, the more they stay the same).
To some extent, that is true, as you could consider today’s Big Data as a derivative of yesterday’s VLDBs (very large databases) and Data Warehouses. One of the biggest changes, IMO, is the shift away from Star Schemas and practices implemented for performance reasons, such as aggregation of data sets, using derived and encoded values, using surrogate and foreign keys to establish linkage, etc. Going forward, it may not be possible to have that much rigidity and still be as responsive as needed from a competitive perspective.
There are many dimensions to big data: A huge sample of data (volume), which becomes your universal set and supports deep analysis as well as temporal and spatial analysis; A variety of data (structured and unstructured) that often does not lend itself to SQL based analytics; and often data streaming in (velocity) from multiple sources – an area that will become even more important in the era of the Internet of Things. These are the “Three V’s” people have talked about for the past five years.
Like many people, my interest in Object Database technology initially waned in the late 1990s. That is, until about four years ago, when a project at work led me back in this direction. As I dug into the various products, I learned they were alive and doing well in several niche areas. That finding led to a better understanding of the real value of object databases.
Some products try to be “All Vs to all people,” but generally, what works best is a complementary and integrated set of tools working together as a service within a single platform. It makes a lot of sense. So, back to object databases.
One of the things I like most about my job is the business development aspect. One of the product families I’m responsible for is Versant. With the Versant Object Database (VOD – high performance, high throughput, high concurrency) and Fast Objects (great for embedded applications like kiosks). I’ve met and worked with brilliant people who have created amazing products based on this technology. Creative people like these are fun to work with, and helping them grow their business is mutually beneficial. Everyone wins.
An area where VOD excels is with the near real-time processing of streaming data. The reason it is so adept at this task is the way that objects are mapped out in the database. They do so in a way that essentially mirrors reality. So, optionality is not a problem – no disjoint queries or missed data, no complex query gyrations to get the correct data set, etc. Things like sparse indexing are not a problem with VOD. This means that pattern matching is quick and easy, as well as more traditional rule and look-up validation. Polymorphism allows objects, functions, and even data to have multiple forms – something else that mirrors real life (just think about the variations of a peripheral device called a “printer”).
VOD and products like it do more by allowing data to be more, which is ideal for environments where change is the norm, such as: Cyber Security; Fraud Detection; Threat Detection; Logistics; and Heuristic Load Optimization. In each case, performance, accuracy, and adaptability are the key to ongoing success.
The ubiquity of devices generating data today, combined with the desire for people and companies to leverage that data for commercial and non-commercial benefit, is very different than what we saw 10+ years ago. Products like VOD are working their way up that “Slope of Enlightenment” because there is a need to connect the dots better and faster – especially as the volume and variety of those dots increases.
It is not a “one size fits all” solution, but it is often the perfect tool for complex data. More importantly, it is another tool to use in an ever-expanding data ecosystem.
These are indeed exciting times!
Spurious Correlations Follow-up
In an earlier post, I wrote about spurious correlations. Over the weekend, I ran across a site that focuses on finding and posting amusing, spurious correlations. While the posts are intended to be funny, they make some very valid points. So, check it out, let me know what you think, and have some fun!
Big Data – The Genie is out of the Bottle!
Back in early 2011, other members of the Executive team at Ingres and I were betting on the future of our company. We knew we needed to do something big and bold, so we decided to build what we thought the standard data platform would be in 5-7 years. A small minority of team members didn’t believe this was possible and left, while the rest focused on making it happen. We made three strategic acquisitions to fill gaps in our Big Data platform. Today (as Actian), we have nearly achieved our goal. It was a leap of faith back then, but our vision turned out to be spot-on, and our gamble is paying off today.
My mailbox is filled daily with stories, seminars, white papers, etc., about Big Data. While it feels like this is becoming more mainstream, reading and hearing the various comments on the subject is interesting. They range from “It’s not real” and “It’s irrelevant” to “It can be transformational for your business” to “Without big data, there would be no <insert company name here>.”
What I continue to find amazing is hearing comments about big data being optional. It’s not – that genie has already been let out of the bottle. Incredible opportunities await companies that understand and embrace its potential. I like to tell people that big data can be their unfair advantage in business. Is that really the case? Let’s explore that assertion and find out.
We live in the age of the “Internet of Things.” Data about nearly everything is everywhere, and we have tools to correlate it and understand so many things (activities, relationships, likes and dislikes, etc.). With smart devices that enable mobile computing, we have an extra dimension: location. And, with new technologies such as Graph Databases (based on SPARQL), graphical interfaces to analyze that data (such as Sigma), and identification technology such as Stylometry, it is getting easier to identify and correlate that data. Someday, this will feed into artificial intelligence, becoming a superpower for those who know how to leverage it effectively.
We are generating increasingly large volumes of data about everything we do and everything going on around us, and tools are evolving to make sense of that data better and faster than ever. Organizations that perform the best analysis, get answers fastest, and act on that insight quickly are more likely to win than organizations that look at a smaller slice of the world or adopt a “wait and see” posture. So, that seems like a significant advantage in my book. But is it an unfair advantage?
First, let’s remember that big data is just another tool. Like most tools, it can be misused and abused. Whether a particular application is viewed as “good” or “bad” depends on the goals and perspective of the entity using the tool (which may be the polar opposite of the groups targeted by those people or organizations). So, I won’t try to judge the various use cases; instead, I’ll present a few and let you decide.
Scenario 1 – Sales Organization: What if you could understand what you were being told a prospect company needs and had a way to validate and refine that understanding? That’s half the battle in sales (budget, integration, and support/politics are other key hurdles). Data that helped you understand not only the actions of that organization (customers and industries, sales and purchases, gains and losses, etc.) but also the stakeholders’ and decision-makers’ goals, interests, and biases. This could provide a holistic view of the environment and allow you to provide a highly targeted offering, with messaging tailored to each individual. That is possible, and I’ll explain soon.
Scenario 2 – Hiring Organization: Many questions cannot be asked by a hiring manager. While I’m not an attorney, I would bet that State and Federal laws have not kept pace with technology. And while those laws vary state by state, there are likely loopholes allowing public records to be used. Moreover, implied data that is not officially considered could color a hiring manager’s or organization’s judgment. For instance, if you wanted to “get a feeling” that a candidate might fit in with the team or the culture of the organization or have interests and views that are aligned with or contrary to your own, you could look for personal internet activity that would provide a more accurate picture of that person’s interests.
Scenario 3 – Teacher / Professor: There are already sites in use to search for plagiarism in written documents, but what if you had a way to make an accurate determination about whether an original work was created by your student? Some people will do the work and write a paper for a student for a fee. So, what if you could not only determine that the paper was not written by your student but also determine who the likely author was?
Do some of these things seem impossible or at least implausible? Personally, I don’t believe so. Let’s start with the typical data our credit card companies, banks, search engines, and social network sites already have about us. Add to that the identified information available for purchase from marketing companies and various government agencies. That alone can provide a pretty comprehensive view of us. But there is so much more that’s available.
Consider the potential of gathering information from intelligent devices accessible through the Internet, your alarm and video monitoring system, etc. These are intended to be private data sources, but history has taught us that anything accessible is subject to unauthorized access and use (just think about the numerous recent credit card hacking incidents).
Even de-identified data (medical/health/prescription/insurance claim data is one major example), which receives much less protection and can often be purchased, could be correlated with a reasonably high degree of confidence to understand other “private” aspects of your life. The key is to look for connections (websites, IP addresses, locations, businesses, people), things that are logically related (such as illnesses/treatments/prescriptions), and then accurately identify (stylometry looks at things like sentence complexity, function words, co-location of words, misspellings and misuse of words, etc. and will likely someday take into consideration things like idea density). It is nearly impossible to remain anonymous in the Age of Big Data.
There has been a paradigm shift in the practical application of data analysis, and companies that understand and embrace it will likely perform better than those that don’t. This technology also raises new ethical considerations and will likely bring new laws and regulations. But for now, the race is on!
Getting Started with Big Data
In Sales, I have the opportunity to speak with many customers and prospects about many things. Most are interested in Cloud Computing and Big Data, but they often don’t fully understand how to leverage the technology to maximize the benefits.
Here is a simple three-step process that I use:
1. For Big Data, I explain that there is no single correct definition. Because of this, I recommend that companies focus on what they need rather than what to call it. Results are more important than definitions for these purposes.
2. Relate the technology to something people are likely already familiar with (extending those concepts). For example: Cloud computing is similar to virtualization and has many of the same benefits; Big Data is similar to data warehousing. This helps make new concepts more tangible in any context.
3. Provide a high-level explanation of how “new and old” are different and why new is better using specific examples that they should relate to. For example: Cloud computing often occurs in an external data center – possibly one where you may not even know where it is- so security can be even more complex than in-house systems and applications. It is possible to have both Public and Private Clouds, and a public cloud from a major vendor may be more secure and easier to implement than a similar system using your own hardware;
Big Data is a little bit like my first house. I was newly married, we anticipated having children, and also anticipated moving into a larger house in the future. My wife and I started buying things that fit into our vision of the future and storing them in our basement. We were planning for a future that was not 100% known.
But our vision changed over time, and we did not know exactly what we needed until the end. After 7 years, our basement was very full, and it was difficult to find things. When we moved to a bigger house, we did have a lot of what we needed. But we also had many things that we no longer wanted or needed. And, there were a few things we wished that we had purchased earlier. We did our best, and most of what we did was beneficial, but those purchases were speculative, and in the end, there was some waste.
How many of you would have thought Social Media Sentiment Analysis would be important 5 years ago? How many would have thought that hashtag usage would have become so pervasive in all forms of media? How many understood the importance of location information (and even the timestamp for that location)? I guess it would be less than 50% of all companies.
This ambiguity is both a good and bad thing about big data. In the old data warehouse days, you knew what was important because this was your data about your business, systems, and customers. While IT may have seemed tough in the past, it can be much more challenging now. But the payoff can also be much larger, so it is worth the effort. You often don’t know what you don’t know – and you just need to accept that.
Now we care about unstructured data (website information, blog posts, press releases, tweets, etc.), streaming data (stock ticker data is a common example), sensor data (temperature, altitude, humidity, location, lateral and horizontal forces), temporal data, etc. Data arrives from multiple sources and likely has multiple time-frame references (e.g., constant streaming versus updates with varying granularity), often in unknown or inconsistent formats. Someday soon, data from all sources will be automatically analyzed to identify patterns and correlations and gain other relevant insights.
Robust and flexible data integration, data protection, data governance, and data privacy will all become far more important in the near future! This is just the beginning for Big Data.
Where Ideas, Experiences, and Lessons Learned Intersect 