Given the vast volume and variety of data that CPG companies manage, ensuring the accuracy and reliability of data is critical. All digital transformation and personalization efforts would fail if data underneath is of poor quality, siloed and delayed. Using machine learning within modern data management platform not only helps determine and improve data quality but also enriches the data with relevant insights and provides intelligent recommended actions for data quality and operational improvements. For example, if you are running a campaign for a major product launch, you can eliminate consumer profiles with low data quality (DQ) scores.
2. Be Agile with Multi-model Data Management
Using legacy tools built on relational databases are too rigid and inflexible, making it difficult to support the dynamic needs of a modern business. For example, adding new data sources or attributes to the customer profiles can result in costly data migration projects. Another challenge is the inability to manage the relationships between various data entities, such as people, products, organizations and places. Modern data-driven CPG brands prevent big data indigestion by using a multi-model, polyglot storage strategy to store and efficiently manage the right data in the right storage. It helps them deliver faster and higher business value from their varied data assets.
3. Leverage the Power of Multi-domain
With “single domain” Master Data Management (MDM), each data entity type has its own unique data store and business logic. On the other hand, a Modern Data Management Platform manages multi-domain (customer, products, stores, suppliers) master data along with transaction and interaction data, third-party, public and social data. Its graph technology makes it easy to describe and visualize complex, many-to-many relationships among customers, products, stores and locations for faster and reliable decision-making. For example, with the help of a graph, CPG brands can rapidly traverse links between consumers, products, purchases, and ratings to make personalized recommendations. They can also tell if the visitors and shoppers browsing their website are from the same household or not.
4. Uncover New Business Models
“Servitization” of products is commonly seen in consumer categories such as music (iTunes and Spotify) and books (Amazon Kindle) but also in business services such as Xerox moving from photocopiers to document services. Historically, CPG companies have been resistant to the move from products to services. Their relationship with their consumers has often been mediated via retailers. Modern data-driven CPG brands often bypass retailers and sell directly to customers (DTC). For example, Dollar Shave Club is offering a monthly subscription to deliver razors and other personal grooming products by mail. This gives them the opportunity to engage directly with their customers, to collect interaction data, and to expand their digital footprint.
5. Explore New Data Partnerships
Data is an enabler of innovation. To keep up with the rapid pace of digital transformation, CPG brands need to develop a culture of collaboration and pursue intra and extra-industry partnerships. They need to recognize that many new entrants are not simply additional competitors. Instead, they represent possibilities for completely new types of business models that over time will blur traditional distinctions between retailers and manufacturers.
6. Augment Decision Management with Artificial Intelligence (AI)
Data-driven CPG companies look at AI through the lens of three business capabilities: automating business processes, gaining insight through data analysis, and engaging with customers and employees. They constantly innovate and disrupt by embracing new technologies to meet the high expectations of consumers. A Modern Data Management Platform coupled with Machine Learning enables contextual information and helps consumer brands answer high-impact business questions such as – Will my customer buy this product or not? Is this review written by a customer or a robot? Which category of products is most interesting to this customer? And so on.
7. See GDPR Compliance as an Opportunity to Improve Customer Experience
CPG brands will be required to be more transparent about how they use consumer data. New regulations like GDPR and increased oversight has important implications in terms of regulatory compliance, product development and marketing messages. Moreover, there are increasing consumer demands for transparency on how companies perform when it comes to sustainability and corporate social responsibility as well as where products are made. A Modern Data Management Platform as a Service (PaaS) helps you create a complete consumer profile with full data lineage, governance, and workflows to continuously manage consumer rights and consents.
Consumer brands are facing unsteady growth, tightening profit margins, complex regulations, and growing competition from lower cost private label brands. Adopting these seven habits would help them reverse the digital curse, achieve hyper-personalized customer engagement, and stay ahead of competition.
DISCLAIMER: I have no inside knowledge into what may or already have been discussed, or data analyzed by either AMC Theaters or MoviePass. This article is purely based on my thoughts as a movie-goer, marketer, and product manager.
“AMC Theatres has threatened MoviePass with a lawsuit, less than a day after the subscription cinema service dropped its subscription fees to $9.95 a month, reports Variety. That means subscribers are able to watch one movie every day for a month for only $9.95. MoviePass would still have to pay AMC full ticket prices each time someone uses the subscription, though. An average ticket is priced at $9.33, so a subscriber would only need to attend two movies a month to put MoviePass at a loss.
In 2016, the service started at $15 per month and ran up to $50 per month for unlimited movies in bigger cities. AMC, which is the largest theater chain in the US said in a statement that MoviePass’ model is unsustainable. The company argued that ticket prices below $10 a month over time wouldn’t be able to generate enough cash to operate quality theaters, nor produce enough income that would allow film makers to make movies of value.” – Source The Verge
I never knew about MoviePass as a subscription for unlimited movies. As a father of twins my wife and I barely get to go to the movies but once every 2 months, and it costs us an extra $100 in babysitting to go, and there’s nothing really compelling as far as “good” movies in our opinion, but I digress! Moviepass’ offer of $9.95 a month does seem to be very compelling, and ultimately very disruptive.
My first reaction in seeing that AMC is suing MoviePass for this action is to wonder out loud whether AMC had gone to MoviePass and offered to jointly analyze their respective datasets in order to see if there might be synergies in such an action.
An Outsiders Product Manager’s Perspective:
Showtimes of movies (beyond opening week of new blockbusters) are rarely full, meaning there is unused inventory in every single time slot
Pricing strategies to try and fill these slots don’t appear to have changed much beyond off peak time discounted ticket offers
Loyalty and rewards programs have now started to become more prevalent so efforts are ongoing to capture consumer profiles
Concession sales per customer are lucrative with a large popcorn and drink often costing more than the standard ticket (letting MoviePass fill shows to capacity could yield more in concession revenues than tickets itself)
Clearly I would need more data to find patterns and analyze this information to form the right conclusions. The steps would be to:
Form a Reliable Data foundation – leading to a 360-degree view of the consumer/movie-goer profile, with demographics, attribution, captured in part through AMC’s loyalty programs, but also could then be cross-referenced (Matched and Merged) with MoviePass’ subscribers to enrich both data sets.
Benefit from Commercial graph technology to find friends and family affiliations to drive offers (see marketing perspective later) to make it more of a social/group movie-going experience
Generate Relevant Insights – by bringing together the transactions processed via the tickets bought through MoviePass vs. walk-ins, and other avenues such as Fandango, promotions etc. Stanalone Master Data Management profiles are insufficient as the real valuable insights are in the transactions/behaviors exhibited by those movie-goers, and they need to be analyzed and seamlessly aggregated back into the master profiles for marketing segmentation
Deliver Recommended Actions – So marketing teams can jointly highlight how AMC and MoviePass could gain synergies from the increased traffic to theaters. Applying machine learning and data science to the reliable data foundation, not just at a macro-level, but to generate the right programs that can take advantage of the identified profiles, to drive more personalized experiences, and revenue-generating concession sales
Leverage Data as a Service – to securely share insights between AMC and MoviePass, preserve consumer privacy, and to bring in more data from suppliers of concessions to negotiate discounts and for synergies such as just-in-time ordering to improve margins
An Outsiders Marketing Perspective:
Once all this data is aggregated, made reliable, and analyzed, the joint market teams of AMC and MoviePass could work on promotions and programs using data-driven applications. With a Modern Data Management foundation they would be able to correlate Recommended Actions back to actual outcomes. Personalizing and improving customer experiences are just the cusp of benefits that can be realized. New business models that could easily be supported might include:
Making it more of a social experience –convert real-estate into Starbucks-like hangouts, with good coffee, wireless, and a place to meet. Offer better higher-end desserts so people come 30 mins before the movie with family and friends after dinner, or stay afterwards to chat about the movie and what they thought about it
Increased kids focus – more tie-ins and kids activities, pre- and post-movie with merchandise sales in a movie “store” with branded items tied-ins. Sales immediately after the event for instant gratification is the a way to command a premium over online sales and their lower prices
Given the fact that VOD, Netflix, Virtual, and Augmented Reality are literally right in the face of and challenging the movie theater going experience, AMC and other theater operators face being disrupted. A Modern Data Management Platform as a Service is essential to not only improve revenue, margins and partner better, but possibly survive.
How do you think the experience could be improved as a movie-goer?
As a Product Manager, how would you use data to gain better insights and possibly partner better. Have you used shared data and insights in similar situations between partners, or perhaps in M&A scenarios? Please share.
Reltio Partner Ecosystem Shines Brightly at Inaugural Modern Data Management Summit
The Reltio hosted Modern Data Management Summit aka, #DataDriven17, industry conference conducted earlier this week @ the JW Marriott, San Francisco was a grand success. We had 300+ attendees, which for an inaugural event is incredible! Kudos to the Reltio marketing team pulling off this conference and doing it in style.
The remarkable Reltio partner ecosystem was there in full force. I am extremely proud of their support and participation. The knowledge and content rich material that was shared by the Reltio partners garnered positive feedback from all attendees. There was a lot of buzz and excitement around the data-driven sessions from industry experts from companies such as IBM, HP, AstraZeneca, Optum, TiVo, ClubCorp, Shutterfly, Nokia, Tapjoy, and Kaiser Permanente, as well as technology partner sessions listed below:
The NoSQL Database Revolution is Just Beginning – Matt Pfeil, Co-Founder, DataStax
Teeing up Data to Drive Business Agility & Customer Self Service – Neil Cowburn, CEO, iMiDiA
Beyond Bitcoin. Disruption and Opportunities with Blockchain – Richie Etwaru, Chief Digital Officer, QuintilesIMS
AWS on the Future of Cloud Computing and Modern Data Management – Asit Sharma Solution Architect, Amazon Web Services
Modern Data Integration for the Cloud – Ravi Dharnikota, Head of Architecture, SnapLogic
How to Generate Powerful Customer Insights with CRM & MDM – Asit Sharma & Sid Mazumdar, Salesforce.com
Tackling Unstructured Data Analytics with AI & Machine Learning – Slater Victoroff, CEO, Indico
How to Master Big Data in the Cloud – 5 Stages of Transformation – David Hsieh, SVP, Marketing at Qubole
We also had our services and solution partners present compelling topics on how Modern Data Management solutions are transforming customer’s business, leveraging next-gen platforms like Reltio:
Improving Customer Experiences with Real Insights from Modern Information Foundations – Poornima Ramaswamy VP, Analytics & Information Management, Cognizant
Digital Transformation Re-imagined – Jim Lalonde, Managing Director, Accenture
The Future of Insight Driven Customer Engagement in Healthcare – John Busalacchi, QuintilesIMS
The Democratization of MDM in Life Sciences – Eric Letts, VP, Master Data, HighPoint Solutions
The Account 360 Ultimatum – Ryan Hartley, Managing Consultant, Infoverity
Leveraging Data-Driven Strategies to Improve Commercial Effectiveness & Generate Reliable Business Insights – Abhi Parab, CEO, Parab Consulting
Why M&A Love’s the Cloud – Scott Holcomb, Principal Deloitte
Terrific panelists from Cognizant, Wipro, LumenData, D&B, LexisNexis, Loqate and GlobalSoft
I was the happy recipient of quite a few compliments about the strength of the Reltio partner ecosystem and also how advanced the partner program is, compared to not just other similar sized companies but also to other established companies. Helps when we have a strong Foundation. Speaking of which, here’s the list of Reltio Foundation Partners that were recognized at at the event:
1. Consolidate and cleanse data from various sources:
Retailers want to bring data together from multiple internal, third party subscriptions, public, and social sources to create a complete and accurate view of their customers. They want to uncover relationships, not just between consumers and products, but locations and family members as well to solve the householding issues. They want a single source of truth of customer data across functional areas and a reliable data foundation for accurate customer segmentation and identification of the influencers.
There were several discussions around retailers wanting to blend interaction data from various channels with consumer profile information, giving marketing, e-commerce, and customer support teams visibility into customer preferences, product interests, and channel choice. Retailers want to deliver insights like churn propensity, lifetime-value, and abandonment rates to relevant teams in the context of their role and objectives. Furthermore, many leading retailers are leveraging machine learning and predictive analytics to suggest next-best-actions to send relevant and consistent information, across all channels, to the customer and find opportunities for up-sell and cross-sell. However, there is still a concern about the reliability and completeness of the data utilized to run such analytics.
3. Create a global product master:
Several retailers want to create a complete product or SKU profile to understand the supply chain relations, contract adherence, consumption per location, overall global business value and even social sentiments about their brands. They want a worldwide real-time view of the product, especially during a launch, to gain critical insights into accurate targeting and managing key influencers in the marketplace, designing relevant promotions and devising social media strategy.
4. Break data silos across departments:
Retailers are looking for ways to encourage collaboration across teams, in real time. With global multi-functional teams, multi-product portfolio, and big data scale consumer information, it is critical to allow as well as secure access to a convergence of information, with the proper level of role-based access and visibility. Data management has to be a shared responsibility across all functional groups and tools for social curation of internal data in the form of annotating, workflows, tagging, and voting allow every member to contribute and continuously improve data quality and the enterprise knowledge.
5. Exchange data with external parties:
There were some interesting discussions about the possibility to share the data externally with the suppliers to establish a single holistic view of the supply chain. Historically, most retailers do not have the infrastructure to process and make transaction-level data accessible easily. Fortunately, this technology is now available as Data as a Service (DaaS). Retailers can efficiently carve out a data view in the cloud and share it with partners or even monetize their data to create new revenue streams. The advantages of retail data sharing include improving on-shelf availability, better demand forecast accuracy, and improving the customer experience, among many others.
6. Be compliant:
With so many teams working with consumer data, retailers need comprehensive auditing and tracking features to guarantee compliance. They want a historical trail for any data merged or updated and want to get alerted to abnormal data viewing patterns by application users for possible information breach or theft. Compliance and transparency need to be inbuilt into the data management rather than treated as reports developed as an afterthought.
According to a McKinsey study, the continued adoption and development of big data levers have the potential to increase US retail productivity by more than 0.5 percent a year through 2020. Such a boost in profitability is especially significant in a sector where margins are notoriously tight.
5 Reasons Cloud MDM will Save you Money, and 5 that will Save Your Business
The benefits of cloud computing have been written up and proven in every way imaginable. You need only to look at the rapid growth and ascension of popular consumer sites such as Facebook, LinkedIn, and more recently Uber and AirBnB to see that cloud infrastructure delivers both economic value and business agility.
For those of us in the B2B enterprise space, proof points abound from Salesforce.com to Workday and others, it’s undeniable proof that the efficiencies, and cost savings of not just cloud, but multi-tenant public cloud is what is powering the business economy, and driving competitive advantage.
Master Data Management (MDM) as a segment in particular has been reported by some analysts as slow to adopt cloud. Some even believe that cloud MDM = hosted MDM, with “lower cost of entry” driven by no hardware to buy or upgrade. Unfortunately major companies like IBM and Informatica have done nothing to dissuade this notion, continuing to offer cloud offerings that are nothing more than “wolf in sheep’s clothing”.
Their reasoning is based on an incorrect premise that customization/configuration, and in other words the business agility, and capabilities of true cloud MDM solutions lag behind that of legacy on-premises offerings that are 10+ years old.
That may be true for vendors who have decided to deploy cloud MDM offerings that essentially have focused on cost reduction. Even giving them names like “Cloud Edition”. But they are missing the mark. Just as Salesforce.com delivered a better CRM than Siebel, Workday a better HR than Peoplesoft, cost is just the tip of the iceberg.
In that spirit, we have separated out the 10 reasons into 5 that are savings related, but 5 than can actually save your business.
5 TCO SAVINGS
1. No Hardware (or Software) to Buy or Maintenance to Pay
This is a no brainer, you can justify this on the back of a napkin in about 10 seconds. An annual subscription fee replaces upfront capital outlay, enough said.
2. No Impact Upgrades
The key here is the cost of upgrades. Like Salesforce.com, true multitenant public cloud MDM providers are able to deliver new functionality on a regular basis (usually 3 times a year) with no impact to existing customers. You get a message saying your system will be upgraded tonight, and the next morning you get access to new features, some of which you selectively choose to enable. Contrast this with the millions of dollars that continue to be spent on legacy upgrades of on-premises MDM tools, and the ongoing angst of expiring support agreements on older releases.
Hosted offerings will also claim this level of support, but the reality is that running an on-premises platform in the cloud, does not make it more easy to upgrade.
3. No ObSOLESCENCE – Technology Insurance Included
What premium would you have paid if you could have bought insurance that paid you out as follows:
Sound too good to be true? The best cloud platforms are modeled after Internet giants, with configurable metadata, offering complete separation of capabilities from the underlying technology foundation. Allowing them to take advantage of the latest offerings, to deliver new functionality on behalf of their customers without missing a beat. Insurance included.
Hosted offerings that also claim this level of support again face similar constraints as the on-premises version. Just running it in a public cloud through managed services does not change any architectural constraints that may exist.
4. No Waste
When you initially size the hardware capacity for your legacy MDM tool, you’ll often have to budget for projected max capacity, this could lead to “over-purchase-ritus” (I have this problem every time I visit Costco). With cloud, you get to elastically scale up and scale down processing, storage and software user licensing. You pay for what you need and use, no more, no less. That is more predictable and fair.
5. NO MORE SERVER BABYSITTING
Cloud MDM should alleviate you from having to worrying about backups, archiving, downtime. The best cloud platforms exceed 99% in their SLA commitments. You get peace of mind and can free up resources to focus on other areas that need attention. This is table stakes. If you are offered hosted MDM in the cloud, make sure the SLA guarantees are up to your business requirements.
5 BUSINESS VALUE BENEFITS
6. Faster Data Onboarding
One of the biggest challenges of MDM is not necessarily just running the matching and cleansing of data from numerous sources, but actually getting the data into the MDM tool in the first place.
For 10+ years data has been acquired/purchased from 3rd parties such as Dun and Bradstreet and IT has had to set up processes to load data in batches into the “hub”. Complete cloud platforms now offer Data as a Service (DaaS), providing an always on, searchable connection between 3rd party data and the data management platform. The same connection also provides access directly to the fingertips of business users on mobile devices. Amazon-style one click acquisition of data is now possible, but only in the cloud. The savings in efficiencies and data delivery are priceless.
For modern data management platforms, data-driven applications are part of the complete solution. There is zero integration! Legacy applications that require up-to-date data pushed downstream can benefit from cloud MDM’s next generation microservices. Eliminating the complexity of java wrappers, and convoluted API formats to deliver and receive data.
7. FASTER BUSINESS ACCESS
With interfaces as easy-to-use as Google, LinkedIn and Facebook, users already know how to use a cloud based data management platform and data-driven applications. Not so legacy tools that merely have been moved to the cloud in a hosted manner.
If you want to find records, you simply type in a free-text search term, just like you do with Google. To find a relationship or hierarchy, the system presents the connected entities (people-to-people, people-to-org, org-to-org, people-to-products) in literally limitless configurations.
To research and bring in data from social sources and 3rd party vendors, a single-click is all that is needed to onboard and blend data together. Need to know what happened to a record or field within a record? See the full audit history, with MS word-level revision history by attribute.
Want statistics on attribute distribution or data quality levels? It’s built into the role-based dashboard.
Need to have better match rules? The system uses machine learning to give you better matches, and can also provide your business users with in context, personalized recommended actions. Productivity is your best cost-buster.
8. FASTER COLLABORATION
Beyond the basics of workflow processes, approvals of changed records, the drudgery of manual data steward verifications, visually reviewing potential matches, there is also the issue of gathering consensus on data. Modern cloud data management platforms include Yelp-style voting and ranking capabilities built-in. This capability supports not just data steward team inputs, but actual business users who are interacting with and leveraging the data daily. Imagine if every single one of your thousands of sales reps are contributing towards your data quality, augmentation and competitive advantage. Again even if cost savings is measured in terms of reduction in effort, shared business value is infinitely more valuable.
9. FASTER 360 INSIGHTS
Cloud MDM is great, but even better when the “M” stands for “Modern” not “Master”. Today MDM is like water and oxygen, critical as a reliable data foundation for a new breed of data-driven applications that deliver relevant insight. It’s the straw that stirs predictive analytics and better decision-making.
So why just focus on Master data profiles and traditional MDM? And why just accept an incremental benefit of hosted MDM in the Cloud when you can give your business the full power of the Cloud.
Cloud is an opportunity to manage all data at scale. Why not have the ability to handle that pesky Big Data (maybe you’ve heard of it?) transactions and interactions, and seamlessly have both a comprehensive data management platform for all data sources, types, at any scale. And have both analytics and operational execution in the same suite of applications! Impossible at any price? Actually you can have it all for less than the cost of traditional on-premises MDM.
10. FASTER BUSINESS VALUE
You’ve heard of the “mythical man month” or “you can only squeeze so much juice out of an orange”. Legacy systems that have fixed data models, stored in 30+ year old relational database technology, cannot deliver business agility, no matter how much money you throw at it, or if you place it into a hosted cloud environment.
The only way to determine if there’s true value is through a TCO assessment of cloud vs. on-premises. Concrete proof that cloud functionality is better than those of their elder, on-premises counterparts. After all, there is no disputing the pure physical cost savings that cloud delivers.
Collectively the time-to-value of a cloud-based solution is what is needed to stay competitive. Those that stay on-premises are tempting fate. Cost savings aside, those who want to avoid going down the path of businesses like Kodak, Blockbuster and others need to embrace cloud for modern data management at any cost. It will literally save your business.
Why Life Sciences Must Go Beyond Master Data Management (MDM) …. Again … with Big Data Analytics
The annual CBI conference in Philadelphia will take place Nov 3-4 at the Wyndham. As in the previous 2 years the topics center around customer (HCP) master data. In fact, this year the conference has been renamed (yet again) from Commercial Data Congress (2013) to Master data Management (2014) to now Enterprise Customer Data Integration and Innovation (2015). It’s ironic because no one has really used the term Customer Data Integration (CDI) since 2005 when Gartner decided MDM was the better moniker.
For the first time Reltio will be participating at the CBI event and we look forward to shaking things up a bit with our participation in workshops, panels about open payments, and sponsorship of the overall event.
In addition to participating at CBI, the Reltio team will also be at the Innovation Enterprise Big Data Analytics in Pharma Summit across town on the same days Nov 3-4 at the Rittenhouse Hotel. That event will be all about the latest NoSQL databases, Hadoop and massive data sets, they won’t be focusing on master data, and that too will be a mistake. We believe that a MDM foundation is required for reliable data regardless of big data volumes, and it’s impossible to get relevant insights and recommended actions without it.
Why do we care so much about this topic?
Many of the team here at Reltio formed the nucleus of Siperian (acquired by Informatica in 2010), the leading on-premises MDM tool widely adopted by life sciences companies. Back in 2005, master data management (MDM) was just taking shape and companies used MDM primarily to improve Siebel CRM data quality before upgrading and migrating their on-premises systems. Back then Siperian was preferred by many to the “seamlessly integrated Siebel Universal Customer Master (UCM)” offering, proving that best-of-breed solutions can be superior to integrated offerings that are designed for a single primary purpose.
One of the biggest issues we faced with Siperian (now Informatica MDM) was defining a relational life sciences data model that could capture not only the basic attributes of healthcare professionals and organizations, but represent real-world HCP-to-HCP, HCO-to-HCO and HCP-HCO relationships. Also thrown in for good measure was an emerging need to master product data, product hierarchies, groups and baskets for pricing and competitive analysis, and to feed product information (PIM) systems.
At Siperian we admittedly struggled with basic hierarchy management and performance issues with merge and especially unmerge. While we preached multi-domain and coined the term Universal MDM, we were never completely successful with standalone product master data management, let alone bringing together both customer and product data into a single consolidated Siperian Hub.
Back then, the best databases we had to model and store life sciences entities and their relationships were the likes of Oracle, DB2 and SQL Server. Cloud and big data technologies such as graphs, columnar stores, HBase (on Hadoop) and Cassandra simply weren’t available.
Fast forward to the present, the MDM landscape remains more or less unchanged despite a quantum leap in technology.
Informatica MDM is still an on-premises solution with many of the same challenges we faced while at Siperian
Cloud-based customer master only offerings such as Veeva Network continue to focus on improving CRM data quality. Much like Siebel UCM did for Siebel CRM over 10 years ago
Customer and product masters are still supported through separate siloed hubs, even when built using the same tool. In fact, Gartner continues to publish separate customer and product magic quadrants as if to re-enforce this fact
Master data must still be delivered to data warehouses or operational data stores in order for business users to get a promised “complete view”
MDM systems and tools built on 1990s relational database technologies continue to hinder the ability to model real-world many-to-many-to-many relationships that graph technologies are designed for
For the most part, life sciences companies are no closer to getting basic affiliation management functionality, or their dream of an all encompassing key account management application as they are hindered by legacy MDM tools. Even a new wave of cloud-based MDM solutions do not make things any better. The good news is that life sciences companies can avoid a new kind of MDM (“Making Da-same Mistake”) … again.
The most popular consumer facing applications today such as LinkedIn and Facebook have shown that business facing data-driven applications can be cloud-based, handle multiple data domains, manage structured, unstructured, master, transactional, activity and social data. Companies should expect complete end-to-end modern data management delivered asPlatform as a Service (PaaS) instead of relying on recurring “next generation master data management” promises that remain unfulfilled.
Show Me (and let me Act on) the Data! The Days of Master Data Only Reports are Numbered
For over 10 years master data has been locked away in MDM tools with limited visibility of the profile and state of the data. Even the data stewards, tasked with the manual resolution through workflow queues have had poor feedback from traditional tools as to the state of their most important asset. MDM vendors’ general response to this challenge has been either to:
Expose the hub metadata through a published model and to allow customers to “roll their own insights” via BI tools such as Microstrategy and Business Objects
Leave it to the users to export the data out to a separate data mart or warehouse, or worse excel spreadsheets for review
Typically the lag and latency associated with analyzing the data quality is of concern, since an agile organization has data continuously coming into the system. Like the painting of the Golden Gate bridge, the work is never done. When you reach one end point, you just turn around and start again.
Furthermore, narrowing efforts to focus on the data that needs the most attention has always been a challenge. There is only so much manual effort that can be applied as resources and expertise are limited.
Being able to quickly filter and see data issues, or have the system provide the alerts and recommended actions is a capability that is available in today’s modern data management Platform as a Service (PaaS). And since all of this should be running in a multi-tenant cloud, using a browser-based and mobile interface, any filtering and creation of lists of data can easily be stored as a URL and shared with colleagues for collaboration.
Companies expect not only in line insights and reporting directly from within the tool, they now want access for frontline business users. Not just so they can see the quality of data for themselves, but to allow them to make changes, offer suggestions, and comments at the point of engagement.
These capabilities can only be found in a new generation of data-driven applications. They provide complete transparency as to the quality of the data, feature complete social collaboration and curation functionality at scale, as well as allowing third party data to augment and replenish gaps in quality in real-time via Data as a Service (DaaS). In other words, reporting is nice, but action speaks louder than reports.
Another reason why reporting on just master data is limiting, is the increased demand to have not just customer entities, but product, organization and other entities and affiliations to provide the big picture. Additionally all related transaction and interaction data are required to be more closely tied into profiles, hence the continuing requirement to create data marts and warehouses.
So merely improving the reporting capabilities of the MDM tool is necessary but not sufficient. Frontline business users want operational execution and the ability to immediately correct or provide input about the data from the same interface they are using for their daily operations, not just the ability to retroactively get reports about the data. Better still, the system should have the smarts to provide recommended actions in the context of workflow to guide both business users and data stewards as to what to do next. Or to take action on their behalf.
With all the amazing modern data management technology available, your company should expect and get more by going beyond just MDM, by providing your business teams with fully integrated data-driven applications. The next time someone offers to “Show you the data!”, ask them whether you can do something about it together as a team.
Master data management (MDM) as a discipline and a technology has had its ups and downs over the last 10 years. With significant investment in multi-million dollar projects, many enterprises certainly have the right to expect a more direct impact on the business.
With the explosion of data volumes and the wide adoption of cloud and mobile technologies, frontline business users are starting to expect a new breed of data-driven applications. They are comparing what they have today with the ease-of-use and agility of consumer applications such as LinkedIn and Facebook.
This webinar replay shows how enterprise data-driven applications backed by a modern data management Platform as a Service is breathing new life into MDM, and allowing both IT and businesses to be more agile and effective. It delves into specific industry use cases from Life Sciences, High Tech, Information and Entertainment to illustrate how organizations in these industries are leap frogging competitors by using data as a strategic asset.
Two months ago, R “Ray” Wang, Principal Analyst, Founder and Chairman of Constellation Research, published “Disrupting Digital Business” (DDB). At the end of this blog post, we will be offering you the opportunity to win a signed copy of Ray’s book, so please read-on.
It immediately received universal praise from technology and business leaders alike.
Indeed, this book has it all. Written in an engaging style, with examples and anecdotes that make it easy to relate to and remember.
So why wait four months to write this review? At first I was shocked that I was able to consume the information in the book in less than the time it takes to complete a baseball game. Rather than re-read it again, I decided that the true test of the book would be how much I was able to apply the concepts, and write this review once time had past, without picking the book up again a second time.
So here goes:
Ray starts off by emphasizing “brand promise” as core to a disruptive digital business
Cites Disney and Marriott as examples of companies that do that extremely well
Points out that organizational DNA, not just technology is critical
Mentions Sony’s double walkman (actually sat on their own MP3 technology because they were worried about cannibalizing their own music sales) as an example of incremental innovation vs. Apple iPod as transformational innovation. (Consumers paying 99 times more than free, because of the great iTunes customer experience)
Explores contextual relationships and the digital exhaust (vast amounts of data that allow businesses to understand their customers). Especially relevant to us at Reltio since we have delivered data-driven applications (DDA) that help companies better manage their key accounts, containing complex network of people and organizations
Covers what he calls the “intention driven mindset”, aka predictive analytics, relevant insights and recommended actions through machine learning. Again relevant to us as core capabilities of Reltio Cloud
Brings to life the notion of a Peer-to-peer economy and markets, something we believe in, and have enabled in the context of data sharing through data-as-a-service
Summarizes what it takes to be a disrupting business, including using new technology that plays well with existing legacy systems and processes
And finally the move to data-driven decisions from gut decisions. He references the failures of data warehouses and marts, and even the attempts to use master data management in an effort to solve the problem. Emphasizing that billions have been spent in a repeating technology cycle, with no real success
No doubt I missed quite a few key things in the book through this attempt at four month recap. The fact that I was able to list out some nuggets is a testament to how well the book was written, and how much we at Reltio believe in Ray’s perspectives.
Our vision at Reltio aligns with Ray’s and we believe data-driven applications (DDA), with a modern data management foundation, are needed to join the “Disrupting Digital Business” (DDB) movement. At Reltio, we have begun practicing many of Ray’s concepts, and we use the Reltio Cloud to run our own business. You might say we’re going from DDA to DDB.
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Can I compare your data with other providers before I purchase it?
Can I compare your data with what I already have before I purchase it?
Can I see how it would improve my data before I purchase it?
Can I filter, search through your data by the criteria I select?
Once I find a set of records I like, can i purchase each record individually?
Am I renting/leasing your data, or do I own it?
How can we track what data belongs to you and what attributes we own?
Is your data pre-aligned/cross referenced to other related data? (e.g. do you provide a link/key to tie your customer data to transactions stored by another data provider?)
In the life sciences industry, since physicians information changes all the time. New practices, new affiliation to hospitals, out of date licenses etc. pharma companies must purchase third party data to keep up. However, the way companies get their data hasn’t changed for decades. They buy lists of HCPs and HCOs sight unseen in batches, based on a set of selection criteria, filters or demographics. IT teams then upload the data using ETL (Extract Transform and Load) tools, on a semi-regular basis (weekly or monthly). Because of this business users are often looking at stale or old data. Real-time access to data is generally not possible, and definitely not within the context of the applications that a frontline business user is actively working on.
Big companies with large marketing and sales teams often reach out and contact customers in person or via email, can get first hand knowledge around out-of-date or inaccurate data. Typically they have no easy way of getting updates back to the third party provider. The third party provider would love to be able to receive and verify those updates from their customers, because they would have a whole army of data quality experts working for them, collaborating on the data and keeping it up to date.
Reltio’s third party data partners deliver data directly to frontline business-users in sales, marketing and compliance. They can each search for and access data in real-time through Google-style search queries. The searches go across all data providers based on any criteria, allowing business users to quickly obtain the best data for their business problem. They can even preview the data in a “try before you buy” mode, and comparison shop. Once they find the data that meets their criteria, they can purchase it through a single-click, just like they were shopping on Amazon. The data is blended into their Reltio data-driven application seamlessly and Reltio tracks exactly where it came from with full lineage and history as part of built-in master data management. Reltio also provides companies with a way of collaborating on the quality of the data, and they can communicate this back to the provider in exchange for possible credits. The provider gets more up to date data, another avenue to license their data with greater exposure, and the customer doesn’t get sent bad data repeatedly in batches over and over again so everyone benefits.
Customers need freedom of choice when it comes to data. Enterprises want to bring data from both third party sources, social media, public and combine it with their own internal data from their applications within their organization. They want to be able to rely on data they purchase, and they want a seamless way of blending the data together to uncover relationships between people, products, places and more. Good access to clean data through Data as a Service (DaaS) is an imperative in this new information economy. And the data must be accessible through enterprise-class data-driven applications that business users use everyday. All of this needs to be on a rock solid foundation of modern data management Platform as a Service (PaaS) with full security, privacy and audibility. By asking 12 key questions up front, life sciences companies can get a clear picture of what to expect, without having to resort to a magic eight ball.
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