The Product Data Domain and the 2017 Gartner Data Quality Magic Quadrant

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data-quality-magic-quadrant-2017The Gartner Magic Quadrant for Data Quality Tools 2017 is out. One place to get it for free is at the Informatica site.

As data quality for product data is high on my agenda right now, I did a search for the word product in the report. There are 123 occurrences of the word product, but the far majority is about the data quality tool as a product with a strategy and a roadmap.

The right context saying about the product domain is, as I could distil it based on word mentioning, as follows:

Product data is part of multidomain

Gartner says that the product domain is a part of multidomain support, being packaged capabilities for specific data subject areas, such as customer, product, asset and location.

Some vendors were given thumbs up for including product data in the offering. These were:

  • BackOffice Associates has this strength: Multidomain support across a wide range of use cases: BackOffice Associates’ data quality tools provide good support for all data domains, with particular depth in the product data domain.
  • Information Builders has this strength: Multidomain support and diverse use cases: Deployments by Information Builders’ reference customers indicate a diversity of usage scenarios and data domains, such as customer, product and financial data.
  • SAS (Institute, not the airline) has this strength: Strong knowledge base for the contact and product data domains.

One should of course be aware, that other vendors also may have support for product data, but this is overshadowed by other strengths.

Effect on positioning

Multidomain brings vendors to the top right. Gartner’s metrics means that leaders address all industries, geographies, data domains and use cases. Their products support multidomain and alternative deployment options such as SaaS.

Product data focus can make a vendor a challenger. Gartner tells that challengers may not have the same breadth of offering as Leaders, and/or in some areas they may not demonstrate as much thought-leadership and innovation. For example, they may focus on a limited number of data domains (customer, product and location data, for example). This also means, that missing product data focus keeps vendors away from the top right positioning, which seems to be hitting Pitney Bowes and Experian Data Quality.

Product data will become more important, but is currently behind other domains

Gartner emphasizes that data and analytics leaders including Chief Data Officers and CIOs must, to achieve CEOs’ business priorities, ensure that the quality of their data about customers, employees, products, suppliers and assets is “fit for purpose” and trusted by users.

Organizations are increasingly curating external data to enrich and augment their internal data. Finally, they are expanding their data quality domains from traditional party domains (such as customer and organization data) to other domains (such as product, location and financial data).

According to Gartner, data quality initiatives address a wide variety of data domains. However, party data (for existing customers, prospective customers, citizens or patients, for example) remains the No. 1 priority: 80% of reference customers considered it the top priority among their three most important domains. Transactional data came second highest, with 45% of reference customers naming it among their top three. Financial/quantitative data was third, with 39% of reference customers naming it. The figure for product data was 34%.

In my view, the 34% figure is because not all organizations have high numbers of product data and have major business pains related to product data. But those who have are looking at data quality tool and service vendors for suitable solutions.

Investing In PIM Is Like Investing In Customer Value

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In his fourth guest blog post here on this blog Rajneesh Kumar of Pimcore makes the case for investing in Product Information Management in order to drive customer engagement and sell more:

shutterstock_627467546 (002)

What makes customers to buy a product? Lots of theories are conceptualized regarding the science, art, and psychology of customer behavior. Companies leave no stone unturned to grab customers’ attention and importune them to buy products. But, things are not as simple as it looks like because the buying cycle is becoming more and more complicated with growing options and multiple customer touch points.

Brands must adapt to the new reality, faster. They need to make consistent efforts in all dimensions. During each buying stage, customers formulate a consideration set and evaluate each option on criteria significant to them. Thus, organizations need to consider all aspects of buying cycle to woo more customers. Here are few factors that can play an important part to hook more customers:

Create Desire:

Let’s put aside what’s in consumer’ mind rather than focus on what should be presented to customers. The simpler and authentic product information would be, easier it will be for customers to make a buying decision. The first line of engagement is to infuse trust with reliable information that would influence and attract buyers. It must create desire amongst customers while providing value that is required.

Unlock Full Value:

Every product has its unique value. More explicitly you represent it; better the chances will be to attract customers. Make sure the hidden USP or locked value of your product should come in front of the customers at every channel where customers interact. Every type of product information, its attributes, and its relations make a decisive contribution in buying decision. You must make a conscious effort to reveal the full value and all potential benefits.

Immerse in Omnichannel World:

We live in a world of interconnected systems and brands cannot flourish without taking an omnichannel approach. It means your product information must be seamless across all channels. And, it is something you cannot go wrong with. Every customer demands same experience across all channels. Never let the bad omnichannel experience ruin the party. You must do it when you can do it.

Be Agile:

‘I am the first one who grabbed it.’ Customers love competition amongst themselves. When we add up competitors’ equation, you can understand how competitive the market has become. Delays kill the experience. You always need to make sure your product is introduced timely to get maximum benefits. In today’s scenarios, maintaining and managing spreadsheets won’t work. You got to be agile in today’s time to ensure higher growth. Decrease complexity within the purchase occasion as much as you can.

Investing in Product Information Management:

In the world of digital, product information management is an involving discipline. Product information management in organizations encompasses a wide range of functions. In most centrally managed organizations, product information management is seen as a key functional area alongside other key functions. While product quality, brand, and price, responsiveness, speediness, courtesy may be important in the shopping process, high level of product knowledge may be more important for organizations to streamline the process.

But, it is equally important to have a rock-solid product information management system that facilitates in consolidating scattered data, provides faster-time-to-market and enables omnichannel success, including:

  • Provide trusted, relevant and complete product data that delight customers.
  • Quickly publish consistent and accurate product information across channel for seamless omnichannel experience.
  • Provide a consolidated view of product information to enable sales and marketing teams to cross-sell and up-sell.
  • Consolidate, enrich, translate, and manage product data and other various data such as customer data, vendor data, and other digital assets.
  • Keep track of web, mobile, app, marketplace, and social.
  • Enable Point of Sale integration, digital signage, and web-to-print omnichannel enablement.

The other side of the story is that there is still no clear-cut unanimity about how a product information management solution does good to an organization. Thus, it is essential to understand the critical role of product information in achieving desired business goals. We recommend you start not just by identifying your product information management challenges but also how solving the problems will make your life easier, plus you can run your business better.

Forward-thinking brands understand the need to unleash the disruptive power of consolidated information to drive customer engagement and boost revenue. Thus, the investment in providing better and unique customer value should be deeply considered. It is not just a technology or platform investment; it is a customer investment.

Rajneesh KumarA digital marketer and growth hacker, Rajneesh Kumar is currently marketing manager at Pimcore Global Services (PGS), an award-winning consolidated open source platform for product information management (PIM), web content management (CMS), digital asset management (DAM) and e-commerce. He is well versed with web analytic tools, paid media marketing and has hands on experience on seo techniques, organic promotion and content marketing.

Better Product Specifications Makes you Sell More Online

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Yesterday Mark Leher of Wand, Inc. published a killer LinkedIn Pulse post with the title Five Reasons Detailed Product Specs are Critical for Killer eCommerce.

In here Mark says: “Digitizing and structuring data are critical to any business. Businesses that control and understand their data deliver superior results. Retail is no different. Product specifications are the DNA of your catalog and should be defined and collected so retailers can maximize sales and maximize intelligence.”

I agree. A survey made last year by the Danish E-Commerce Association (FDIH) revealed that 81% of visitors will leave a webshop with incomplete product information.

The key challenges in structuring and digitizing product information for merchants/retailers are:

  • “Customizing an ecommerce taxonomy hierarchy and specifications model and assigning specifications to the specific products” as mentioned as the service provided by Wand in Mark’s post.
  • Connecting to manufactures and pull detailed product information from them in one uniform way despite that manufacturers will provide data in each their ways as described on the page about Product Data Pull.

Sell More Wand

The Good, the Better and the Best Kinds of Data Quality Technology

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If I look at my journey in data quality I think you can say, that I started with working with the good way of implementing data quality tools, then turned to some better ways and, until now at least, is working with the best way of implementing data quality technology.

It is though not that the good old kind of tools are obsolete. They are just relieved from some of the repeating of the hard work in cleaning up dirty data.

The good (old) kind of tools are data cleansing and data matching tools. These tools are good at finding errors in postal addresses, duplicate party records and other nasty stuff in master data. The bad thing about finding the flaws long time after the bad master data has entered the databases, is that it often is very hard to do the corrections after transactions has been related to these master data and that, if you do not fix the root cause, you will have to do this periodically. However, there still are reasons to use these tools as reported in the post Top 5 Reasons for Downstream Cleansing.

The better way is real time validation and correction at data entry where possible. Here a single data element or a range of data elements are checked when entered. For example the address may be checked against reference data, phone number may be checked for adequate format for the country in question or product master data is checked for the right format and against a value list. The hard thing with this is to do it at all entry points. A possible approach to do it is discussed in the post Service Oriented MDM.

The best tools are emphasizing at assisting data capture and thus preventing data quality issues while also making the data capture process more effective by connecting opposite to collecting. Two such tools I have worked with are:

·        IDQ™ which is a tool for mashing up internal party master data and 3rd party big reference data sources as explained further in the post instant Single Customer View.

·        Product Data Lake, a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, merchants and end users of product information. This service is described in detail here.

DQ

Sell more. Reduce costs.

Henrik Gabs LiliendahlLeave a comment

Business outcome is the end goal of any data management activity may that be data governance, data quality management, Master Data Management (MDM) and Product Information Management (PIM).

Business outcome comes from selling more and reducing costs.

At Product Data Lake we have a simple scheme for achieving business outcome through selling more goods and reducing costs of sharing product information between trading partners in business ecosystems:

Sell more Reduce costs

Interested? Get in touch:

← Back

Thank you for your response. ✨

Neutron Star Collision and Data Quality

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The scientific news of the day is the observed collision of two neutron stars resulting in gravitational waves, an extremely bright flash – and gold.

The connection between gravitational waves and Master Data Management (MDM) was celebrated here on the blog when those waves were detected for the first time as told in the post Gravitational Waves in the MDM World.

The ties to Product Information Management (PIM) was examined in the post Gravitational Collapse in the PIM Space.

Now we have seen a bright flash resembling what happens when two trading partners collide, as in makes business together encompassing sharing master data and product information. Seen from my telescope this improves data quality and thereby business outcome (gold, you know) as explained in the post Data Quality and Business Outcome.

Neutron Star Collide

Using Pull or Push to Get to the Next Level in Product Information Management

Henrik Gabs LiliendahlLeave a comment

The importance of having a viable Product Information Management (PIM) solution has become well understood for companies who participates in supply chains.

The next step towards excellence in PIM is to handle product information in close collaboration with your trading partners. Product Data Lake is the solution for that. Here upstream providers of product information (manufacturers and upstream distributors) and downstream receivers of product information (downstream distributors and retailers) connect their choice of in-house PIM solution or other product master data solution as PLM (Product Lifecycle Management) or ERP.

Read more about that in the post What a PIM-2-PIM Solution Looks Like.

The principle behind Product Data Lake is inspired by how a data lake differs from a traditional data warehouse. In a data lake the linking and transformation takes place late, when the data is consumed by the receiver.

pdl-diagram-new

Product Data Lake resembles a social network as you connect with your trading partners from the real world in order to collaborate on getting complete and accurate product data from the manufacturer to the point-of-sales:

  • Pull-PushAs a downstream receiver, you can be on the winning side by utilizing our Product Data Pull service
  • As an upstream provider, you can be on the winning side by utilizing our Product Data Push service

To the Cloud and Beyond

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Over at the Informatica blog Joe McKendrick recently wrote about When It’s Time to Give Data Warehouse a Digital Makeover.

In here Joe examines how data warehouses can be modernized to augment architectures supporting data lakes and Mater Data Management and the case for moving data warehouses to the cloud.

In my view, a lot of data management disciplines will eventually move to the cloud as one follows the other. By adding “beyond” I suggest, that cloud solutions will not only be something that is supported company by company. Eventually you will be able to get business outcome by sharing data management burdens within your business ecosystem.

My current venture called Product Data Lake is an example of such a solution. It modernizes the data warehouse thinking within product information sharing by using a data lake concept in the cloud ready-to-use by trading partners within business ecosystems:

  • If you are a provider of product information, typically as a manufacturer of goods, you can harvest your business outcome by using us for Product Data Push
  • If you are a receiver of product information, you can harvest your business outcome by using us for Product Data Pull

pdl-top

Data Quality and Business Outcome

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take-2The connection between MDM (Master Data Management) and business outcome was discussed on this blog in the previous post called MDM, Reltio, Gartner and Business Outcome.

Now, MDM and Data Quality are closely connected disciplines. So, it was interesting to read today’s post on the Experian Data Quality UK blog, where James Marrable states this: Want to improve performance? Improve your data.

In his section around improving data James, among other things, suggests asking this question: “Do you have other data sources you can bring in to support the data you have?”

This is a key question to me and in my eyes a very important mean to make your data bring business outcome. Applying second party and third party data can increase the potential value of your first party data in these ways:

  • Utilizing third party data to compile complete, accurate and timely party data assets needed for understanding and connecting with customers.
  • Receive second party data to compile complete, accurate and timely product information.
  • Having a holistic view of internal and external data needed for decision making.

Hereby you will sell more, reduce costs and mitigate risks.

MDM, Reltio, Gartner and Business Outcome

Henrik Gabs Liliendahl1 Comment

A recent well commented blog post by Andrew White of Gartner, the analyst firm, debates What’s Happening in Master Data Management (MDM) Land?

The post is an answer to a much liked and commented LinkedIn status post by Ramon Chen, Chief Product Officer of Reltio.

In his post Andrew connects the classic dots: How does technology lead to business outcome? Especially the use of cloud solutions and the multi-tenant aspect is in the focus. Andrew asks: What do you see “out there”?

My view is that multi-tenant is not just about offering the same subscription based cloud solutions to a range of clients. It is about making clients sharing the same business ecosystem work in the same MDM realm. This is the platform described in Master Data Share.

Gartner Digital Platforms 2
Source: Gartner

Oh, and what does that have to do with business outcome? A lot. Organizations will not win the future the race by optimizing there inhouse MDM capabilities alone. With the rise of digitalization, they need to connect with and understand their customers, which I believe is something Reltio is good at. Furthermore, organisations need to be much better at working with their business partners in a modern way, including at the master data level. The business outcome of this is:

  • Having complete, accurate and timely data assets needed for understanding and connecting with customers. You will sell more.
  • Having a fast and seamless flow of data assets, not at least product information, to and from your trading partners. You will reduce costs.
  • Having a holistic view of internal and external data needed for decision making. You will mitigate risks.