Three Remarkable Observations about Reltio

The latest entry on The Disruptive Master Data Management Solutions List is Reltio. I have been following Reltio for more than 5 years and have had the chance to do some hands on lately.

In doing that, I think there are three observations that makes the Reltio Cloud solution a remarkable MDM offering.

More than Master Data

While the Reltio solution emphasizes on master data the platform can include the data that revolves around master data as well. That means you can bring transactions and big data streams to the platform and apply analytics, machine learning, artificial intelligence and those shiny new things in order to go from a purely analytical world for these disciplines to exploit these data and capabilities in the operational world.

The thinking behind this approach is that you can not get a 360-degree on customer, vendor and other party roles as well as 360-degree on products by only having a snapshot compound description of the entity in question. You also need the raw history, the relationships between entities and access to details for various use cases.

In fact, Reltio provides not just operational MDM, but through a module called Reltio IQ also brings continuously mastered data, correlated transactions into an Apache Spark environment for analytics and Machine Learning. This eliminates the traditional friction of synchronizing data models between MDM and analytical environments. It also allows for aggregated results to be synchronized back into the MDM profiles, by storing them as analytical attributes. These attributes are now available for use in operational context, such as marketing segmentation, sales recommendations, GDPR exposure and more.

Multiple Storing Capabilities

There is an ongoing debate in the MDM community these days about if you should use relational database technology or NoSQL technology or graph technology? Reltio utilizes all three of them for the purposes where each approach makes the most sense.

Reference data are handled as relational data. The entities are kept using a wide column store, which is a technique encompassing scalability known from pure column stores but with some of the structure known from relational databases. Finally, the relationships are handled using graph techniques, which has been a recurring subject on this blog.

Reltio calls this multi-model polyglot persistence, and they embrace the latest technologies from multiple clouds such as AWS and Google Cloud Platform (GCP) under the covers.

Survival of the Fit Enough

One thing that MDM solutions do is making a golden record from different systems of records where the same real-world entity is described in many ways and therefore are considered duplicate records. Identifying those records is hard enough. But then comes the task of merging the conflicting values together, so the most accurate values survive in the golden record.

Reltio does that very elegantly by actually not doing it. Survivorship rules can be set up based on all the needed parameters as recency, provenance and more and you may also allow more than one value to survive as touched in the post about the principle of Survival of the Fit Enough.

In Reltio there is no purge of the immediately not surviving values. The golden record is not stored physically. Instead Reltio keeps one (or even more than one) virtual golden record(s) by letting the original source records stay. Therefore, you can easily rollback or update the single view of the truth.

The Reltio platform allows survivorship rules to be customized in rulesets for an unlimited number of roles and personas. In effect supporting multiple personalized versions of the truth. In an operational MDM context this allows sales, marketing, compliance, and other teams to see the data values that they care about most, while collaborating continuously in what Reltio calls the Self-Learning Enterprise.

Going beyond operational MDM

 

There is no PIM quadrant, but there is a PIM wave

2018-Forrester-PIM-WaveWith the, in my eyes well justified, merge of the two Master Data Management (MDM) quadrants Gartner, the analyst firm, is somehow missing some ranking of specialised Product Information Management (PIM) vendors.

However, Forrester, the other analyst firm, still have their wave with the fresh new Forrester Wave™: Product Information Management Solutions, Q2 2018.

Two of the leaders have already announced their position as you can see here with Enterworks and Contentserv.

If you want to know more about the best PIM solutions on the market, you can also read about Enterworks, Contentserv, Stibo Systems and Riversand on the disruptive list of MDM, PIM and DAM solutions.

 

Data Monetization and Data Quality

Traditionally data quality management has revolved around making data fit for purpose in various business processes and thus data quality has contributed indirectly to business outcomes, as the business benefits were measured and harvested by results created in these business processes.

This situation has also made it very hard to create distinct business cases for data quality improvement. Most often data quality improvement and related disciplines and data governance, Master Data Management (MDM) and Product Information Management (PIM) has been part of wider business cases concerning for example Customer Relationship Management (CRM) and eCommerce perhaps under an even wider specific business objective.

In today’s data driven business world and drastic rising top-level appetite for digital transformation we see more and more examples of how data can be used much more directly to create business outcome through new or fundamentally reshaped business services and business models.

WebinarsOne example close to me is how data quality via completeness of product information can lead directly to selling more online as told in the post Where to Buy a Magic Wand?

On the 7th August I will elaborate on these themes in a webinar together with Rado Kotorov. The webinar is hosted by Information Builders and you can learn more and register on the Data Monetization webinar here.

 

Achieving Business Benefits from Multi-Domain MDM

Multi-Side MDMThe title of this blog post is also the title of my presentation at a Master Data Management (MDM) event that will take place in Berlin the 18th and 19th October 2018.

Here, I will give my perspectives on:

Read more about this MDM event from ThinkLinkers here. Hope to see you in Berlin.

PS: You can watch a YouTube video with testimonials from a previous event here.

Ecosystem Wide Product Information Management

The concept of doing Master Data Management (MDM) not only enterprise wide but ecosystem wide was examined in the post Ecosystem Wide MDM.

As mentioned, product master data is an obvious domain where business outcomes may occur first when stretching your digital transformation to encompass business ecosystems.

The figure below shows the core delegates in the ecosystem wide Product Information Management (PIM) landscape we support at Product Data Lake:

Ecosystem Wide PIM.png

Your enterprise is in the centre. You may have or need an in-house PIM solution where you manipulate and make product information more competitive as elaborated in the post Using Internal and External Product Information to Win.

At Product Data Lake we collaborate with providers of Artificial Intelligence (AI) capabilities and similar technologies in order to improve data quality and analyse product information.

As shown in the top, there may be a relevant data pool with a consensus structure for your industry available, where you exchange some of product information with trading partners. At Product Data Lake we embrace that scenario with our reservoir concept.

Else, you will need to make partnerships with individual trading partners. At Product Data Lake we make that happen with a win-win approach. This means, that providers can push their product information in a uniform way with the structure and with the taxonomy they have. Receivers can pull the product information in a uniform way with the structure and with the taxonomy they have. This concept is outlined in the post Sell more. Reduce costs.

The Emperor´s New Term

Emperor_Clothes“No one dared to admit that he couldn’t see anything, for who would want it to be known that he was either stupid or unfit for his post?”

This is a quote from the story called The Emperors New Clothes by Hans Christian Andersen.

Having been in and around the IT business for nearly 40 years I have seen, and admittedly not seen, a lot. Inflated hype has always been there, and a lot of technologies, companies and gurus did not make it, but came out naked.

What will you say are the emperor’s new clothes within data management today. Here are some suggestions:

  • Social MDM (Social Master Data Management): The idea that master data management will embrace social profiles and social data streams. If not anything else, did GDPR kill that one?
  • Big Data: This term has been killed so many times. But were those always a staged murder?
  • Single source of truth: The vision that we can have one single source that encompasses everything we need to know about a business entity. This has been a long time running question. Will it ever be answered?

What is your suggestion?

Where to Buy a Magic Wand?

Sometimes you may get the impression that sales, including online sales, is driven by extremely smart sales and marketing people targeting simple-minded customers.

Let us look at an example with selling a product online. Below are two approaches:

Magic wand

Bigger picture is available here.

My take is that the data rich approach is much more effective than the alternative (but sadly often used one). Some proof is delivered in the post Ecommerce Su…ffers without Data Quality.

In many industries, the merchant who will cash in on the sale will be the one having the best and most stringent data, because this serves the overwhelming majority of buying power, who do not want to be told what to buy, but what they are buying.

So, pretending to be an extremely smart data management expert, I will argue that you can monetize on product data by having the most complete, timely, consistent, conform and accurate product information in front of your customers. This approach is further explained in the piece about Product Data Lake.

Product Data Lake Behind the Scenes

Product Data Lake is a cloud service for exchanging product information between manufacturers, distributors and merchants. When telling about the service I usually concentrate on the business benefits and how the service will make you sell more and reduce costs.

However, there will always be one or two persons in the audience who wants to know about the technology behind. And for sure, this is important too.

The service is built using some of the newest and best-of-breed technologies available for this purpose today. This includes Amazon Elastic Computing Cloud for hosting the public cloud version, MongoDB for storing data, RabbitMQ for handling data streams and ElasticSearch for finding data.

PDL Architecture

You can dive into the geeky parts in this PDF document: Product Data Lake Architecture.

MDM in The Cloud, On-Premise or Both

One of the forms of Master Data Management (MDM) is the rising cloud deployment model as touched in the Disruptive MDM List blog post about 8 Forms of Master Data Management.

If we look at the MDM solution vendors, they may in that sense be divided into three kinds:

  • Cloud only, which are vendors born in the cloud age and who are delivering their service in the cloud only. Reltio is an example of that kind of MDM vendor.
  • Cloud or on-premise, which are vendors that can deliver both in the cloud and on premise, but where it makes most sense that you as a customer chooses the one that fits you the best. An example is Semarchy.
  • Cloud and on-premise. Informatica is the example of an MDM vendor that embraces both deployment models (together with other data management disciplines) at the same time (called hybrid) as told in an article by Kristin Nicole of SiliconANGLE. The title goes like this: Balancing act: Informatica straddles on-prem needs with cloud data at Informatica World 2018

Cloud MDM

Happiness vs Market Strength

When following analyst market reports one thing that always strike me is that the vendors who have charged the most for licenses (being to the right on the market strength axis) seldom are the same as those having the most satisfied customers.

The Data Quality Product Landscape 2018 from Information Difference has no surprises there either.

On the technology vertical axis, the vendors are pretty even, while they stretch out on the horizontal market strength axis.

DQ Landscape 2018

The report states: “The happiest customers based on this survey were those of Datactics followed by ActivePrime”. You will find those to the left.

(Innovative Systems, Experian and Syncsort were the better of the rest it must be said.)

See the full report here.