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Four Ways to Use GDPR as a Strategic Driver

Ankur Gupta, Sr. Product Marketing Manager, Reltio

Post May 25, 2018, per the General Data Protection Regulation (GDPR), companies with business ties to the European Union need to comply to GDPR standards. The cost of non-compliance is huge, but the regulation is meant to benefit individuals as well as businesses. Let’s look at what it can unlock for you and your brand if you approach it in the right way. What about being able to say that you are the safest enterprise in your marketplace when it comes to data? How about if you can not only reduce operational cost but can also create new revenue streams by being compliant to GDPR and other upcoming regulations?

1. Replace Legacy Systems by Future Proof Cloud-based Applications

When companies are taking steps to comply with GDPR, they are required to perform a ‘spring clean’ of their data, which can in turn lead to multiple efficiency gains. Organizing data improves the way firms carry out analytics and take business decisions. To comply with the regulation, companies must be able to illustrate the entire data flow – how data comes into the company; how they store and manage it; and how they treat it at end of life. This will encourage businesses to replace legacy systems by flexible cloud services to be more nimble and transparent especially when regulatory regime keeps evolving. In addition, most large enterprises have grown through M&A. Thus, they can look at GDPR as an opportunity to get rid of obsolete software and accelerate application retirement.

2. Gain Brand Loyalty and Attract New Customers

Companies can leverage GDPR to change the landscape from risk mitigation to improving their long-term competitive advantage. They can see early GDPR compliance as a competitive differentiator and position themselves as leaders of an emerging new normal. We trust those businesses who values our privacy beyond mere legal compliance. Thus, GDPR is an opportunity for businesses to get their data in order, get compliant and become consistently transparent with their customers. In a post-GDPR world, data sharing would be seen in the context of mutual respect and value exchange. It is an opportunity to re-connect your business with your current and potential customer base and start a new relationship based on mutual trust and responsible personalization.

3. Invest for the Future

The criticism that GDPR compliance might restrict innovations in AI ignores a subject’s right to privacy and consent. In fact, not being GDPR compliant would impose far more constraints on data collection and processing, slow down the ability to leverage innovations in AI and pay an opportunity cost such as market share losses in the future. Read this article for more details – Understanding GDPR and Its Impact on the Development of AI. In addition, in an era of data-driven innovation, business partners need to work together across the value chain. Data-driven innovation requires a clear understanding of the data to be collected and the reasons for collecting it. There are double opt-ins in such value chain: both partners need to be clear about what data they have about each other, and why. It’s very important that their data sharing practices are compliant with GDPR and other upcoming data regulations. As a first step to GDPR compliance, companies must define the scope of GDPR-relevant personal data that is collected, processed, and shared. Once a company identifies the scope of GDPR-relevant personal data, it should catalog all internal and external data sources that fall within this scope.

4. Execute A Delicate Interplay of Offense & Defense Data Strategies

In the post-GDPR era, personal data protection will become a data strategy issue. To comply, businesses need to have solid data organization and data governance in place. The GDPR gives companies the opportunity to holistically re-assess all their data, not just personal data. Data defense is about minimizing regulatory risk and preventing theft. Data offense focuses on increasing revenue, profitability, and customer satisfaction. Strong regulation in an industry (e.g. financial services or health care) would move the company toward defense; strong competition for customers would shift it toward offense. The CDOs and the rest of the leadership should see GDPR as an opportunity to establish the appropriate trade-offs between defense and offense to support company’s overall strategy. Read this blog post for more details – Is Your Data Strategy Defensive or Offensive? Why Not Both?.

Data is a company’s most important asset, and it’s constantly growing. Taking mandated compliance and turning it into an opportunity to personalize, delight and exceed customer expectations would fuel innovation reliably and responsibly.

Three Critical Ingredients for AI, Machine Learning & Cognitive Computing Success

Ramon Chen, Chief Product Officer, Reltio

You may already know a lot about Artificial Intelligence (AI), Machine Learning (ML), Deep Learning or even what some vendors call Cognitive Computing. Or maybe you are still trying to understand the nuances and differences between each term, and how they relate to each other.

Either way, it’s easy to be seduced by the “black magic” of technology that can solve a variety of your business challenges by just asking Watson, Einstein, Siri, Alexa, Hal (click for the iconic HAL 9000 scene in Space Odyssey) or other “humanizing” names.

In fact Gartner’s 2016 Emerging Technologies Hype Cycle has Machine Learning at the very “Peak of Inflated Expectations.” Those who are familiar with the cycle, know what is likely to come next <Trough of Disillusionment cough cough>. 

 

If you want to protect yourself from the hype, here are 3 critical ingredients for your consideration:

At Reltio we’ve been articulating a vision, which includes a pragmatic perspective of machine learning (ML) for over 3 years now. Realizing that not only does ML not offer a silver bullet, but there is still much to learn (pun intended) as to how such technologies can ultimately benefit both IT and business. Noted Big Data expert Bernard Marr provides a nice list of use cases that might be applied to your specific business and industry. The key is that a focused set of benefits for each users’ role, must be defined in order for it to be accurately measured so it doesn’t get labelled yet another (data) science project with limited value.

#1 Create a Reliable Data Foundation

Most companies are NOT ready for any form of AI, ML, or Cognitive Computing to help their business user, because their data is such poor shape to even attempt such an endeavor. Ironically, a great IT use case is to use ML to first help improve the consistency, accuracy and manageability for better data quality (DQ), uncovering patterns, anomaly detection and assisting humans, such as data stewards, to make their job more focused and efficient.

#2 Bring Analytics and Machine Learning to the Data

Just as the process of aggregating data to perform historical or predictive analytics is a cumbersome and expensive process, gathering and blending all of the right data that will guarantee machine learning is effective must be the in the DNA of any Modern Data Management Platform as a Service (PaaS).  

Bolting on AI or ML into legacy master data management (MDM) systems, or using such MDM tools to feed downstream disparate ML tools is putting lipstick on hosted managed services disguised as cloud. Reliable data, relevant insights and recommended actions via machine learning needs to be seamlessly combined into one, single multi-tenant cloud platform, architected from the ground up, for both analytical intelligence and operational execution, through data-driven applications.

Successful execution requires a closed-loop of all data, insights and actions, to ensure accurate metrication for continuously improved outcomes. Further, a multi-tenant cloud environment is the only way sufficient storage and processing capacity can be elastically accessed on demand to meet any business need.

Another benefit of a multi-tenant cloud PaaS is the potential to use a wide variety of anonymized data to help with machine learning across all industries. Having a large enough set of data is a critical factor for smaller companies to benefit from the right recommended actions, for common industry use cases.

#3 Don’t Go all in on one vendor

In a rush to market that “our tools do it too,” large vendors will unfortunately, over promise, and under deliver. It’s not their fault, as they must respond to the market, but many face an unenviable task of achieving ingredient #2 above, let alone attempting to now also execute on a plan to deliver their own AI technologies. 

An open ecosystem that allows you to choose and partner with the technologies, and domain experts of your choice is critical to getting the most out of a still young and evolving landscape. Most companies are already trying to evolve out of their legacy MDM platforms. Getting further locked into a single vendor, delivering both MDM and ML, through siloed disparate tools will not provide “clarity,” and may further complicate an already fragmented data management strategy.

At Reltio, we formed strategic partnerships with companies like QuintilesIMS to combine our strengths to jointly deliver on a vision for the next generation MDM and analytics capabilities for life sciences.

In summary, look to master your data in #1 for a reliable data foundation. #2 ensure that it covers all data types, sources and modes of consumption in a seamless feedback loop on a Modern Data Management platform architected from the ground up to avoid further siloing your data. Finally, #3 give yourself the openness and flexibility of your partners of choice to meet your business needs. 

You don’t want a HAL-like failure that prevents you from realizing your true goal of improving your business.