ga-google-analytics domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /www/startwithdataaustralia_539/public/wp-includes/functions.php on line 6260health-check domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /www/startwithdataaustralia_539/public/wp-includes/functions.php on line 6260hreflang-tags-pro domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /www/startwithdataaustralia_539/public/wp-includes/functions.php on line 6260wp-import-export-lite domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /www/startwithdataaustralia_539/public/wp-includes/functions.php on line 6260complianz-gdpr domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /www/startwithdataaustralia_539/public/wp-includes/functions.php on line 6260wordpress-seo domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /www/startwithdataaustralia_539/public/wp-includes/functions.php on line 6260neve domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /www/startwithdataaustralia_539/public/wp-includes/functions.php on line 6260The post PIM for syndication to the digital shelf appeared first on Start with Data.
]]>If you want your digital shelf to gain a competitive edge, using a PIM to syndicate your product data is a no-brainer.
As a brand or manufacturer, you need to guarantee that the ongoing feed of product information to all your distribution channels, sales portals, websites, and touchpoints is faultless.
By integrating your syndication with a PIM System, you can be sure that you’re sending enriched and high-quality information to the right place at the right time. That guarantees minimised time to market and a competitive edge in a fiercely competitive environment.
A PIM allows you to configure your product information to fulfil the requirements of multiple channels. From its centralised storage hub to wherever you want – websites, large marketplaces, mobile-friendly platforms, print or electronic catalogs, in-store POSs, – in fact, all channels. Your content can be consistently updated, tailored, and repurposed at speed. That means personalised, searchable and compelling content to drive consumers to your digital shelf.
As the attributes and capabilities of PIM syndication software grows and extends, the range and variety of product information generated plays a larger role in customers’ pre-purchase experience.
Your syndication tools can be leveraged to create a customer touchpoint which is consistent, comprehensive, up to date, responsive, and user-friendly.
We would love to help your organisation unlock the potential of better product information management – Ben Adams, CEO Start with Data
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]]>The post Barbour Case Study appeared first on Start with Data.
]]>
We have a highly experienced team of retail PIM consultants ready to support your implementation from supplier onboarding through to customer experience
Ben Adams, CEO
We can help get your products onto the digital shelf, with our accelerators
Beth Parker, PIM Consultant
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]]>The post 3 advantages of PIM and the digital shelf for manufacturers appeared first on Start with Data.
]]>The quality of your digital shelf defines not only the volume of your sales revenue, but also the value of your brand to consumers. It is the highest-quality product data which generates essential and relevant information, leading to successful business outcomes on your digital shelf. The more comprehensive and accurate the information, the easier it is for customers to search for, find and buy products. A PIM solution drives these performance enhancements on your digital shelf.
So, let’s look at three key advantages a PIM brings to your digital shelf:
As a brand, you want your products competing in the market as soon as possible. A modern PIM uses state-of-the-art technology to speed up channel lead times. You can enable agile management and bulk editing of all product information. Product time-to-market falls, and information is agile and dynamic with:
Accurate, wide-ranging, and emotive product content is fundamental for a brand’s customer experience. A PIM allows you to deploy enriched, compelling information on your product pages across all touchpoints and channel types: a critical factor in an often-random customer route towards purchase, leveraging a PIM to enhance customer experience brings measurable improvements in conversion rates, with dynamic updates providing more and more opportunities for cross- and up-selling.
Omnichannel, be it on tablets, laptops, PCs, or smart phones is where smart brands are shifting – it can find your customers where they are, rather than where you wish them to be. On the digital shelf, an effective omnichannel experience allows the customer to research and shop across a wide range of eCommerce platforms, major marketplaces, and in-store. And that shopping experience must remain consistent, wherever, or however your product information is accessed.
We have a highly experienced team of retail PIM consultants ready to support your implementation from supplier onboarding through to customer experience
Ben Adams, CEO
We can help get your products onto the digital shelf, with our accelerators
Beth Parker, PIM Consultant
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]]>The post Product Experience Management appeared first on Start with Data.
]]>Over the last two years (since the start of the pandemic), 77% of respondents to a recent survey noted that their digital experience with favoured brands has changed, while 43% are shopping online more compared with early 2019. Such statistics highlight the increasing importance of pursuing a strong product experience management strategy as digital transformation opens up customer experience opportunities in eCommerce.
Product experience management is a management discipline used largely by brands to craft and deliver enriched product information content to wherever it is needed, in a timely way. Its ultimate objective is to drive sales, build loyalty, and grow market share and revenue. That content is rapidly disseminated across the digital commerce ecosystem to create the relevant and compelling experiences that consumers are increasingly demanding.
Product experience relies on strong product information. PXM leverages that information to create usage and emotional data. Understanding this enables users to move beyond managing data to crafting and delivering compelling and persuasive experiences.
The basis for an outstanding customer experience is to have insight into the quality of product experience buyers expect. An artfully executed PXM strategy uses different tools and practices so retailers and brands can deliver an exciting product experience.
A vital part of building a good PXM practice is to identify the subject matter experts (SME) you need. Consider the pivotal members on your PIM team early in the process of establishing the aims of your PXM strategy.
The PIM team will have comprehensive knowledge of your products, data governance processes, target catalog structure, current channels, and physical outlets. The team also needs to liaise with those responsible for systems, such as your ERP, eCommerce store, or DAM, all of which will play a role in your PXM.
Product Information Management (PIM) capabilities form the foundation of excellent PXM. A PIM solution provides the golden record for your product data – so, descriptions, technical attributes, categorizations, and so on, are complete, accurate, up to date, of a high quality and enriched.
To create a great product experience, you should include more than just technical facts. Create a vision of its proven benefits with user reviews, so the consumer can envision the positive outcomes.
Nowadays, the consumer expects a richness of content which plays on emotion and establishes a vision for customer value. They require information in context, such as photos, video, and images. For instance, in addition to a picture of a multi-purpose food processor, include colourful pictures showing these features in use. The use of virtual reality and augmented reality is also likely to become a baseline expectation for exciting and immersive customer and brand experiences.
Ensure mindful adaptation to cultural norms, sensitivities, and local norms and compliance. This means researching and using the appropriate descriptions, images, metrics, and so on.
Low-value, labour-intensive repetitive tasks can be automated to allow marketing and e-commerce teams to focus on high-value tasks like honing product descriptions and digital assets for channel types.
It is key to contextualize your product information to deliver a relevant and compelling experience at every customer touchpoint. A good example is voice assistant technology, whose use is growing. When it comes to something as simple as a text-based product description, a paragraph of product description alongside rich digital assets works for print and websites, whereas for a voice assistant, that description needs to be significantly shorter. people search via voice rather than typing.
The PIM will enable your marketing teams to move through the PXM journey from disparate product data to dynamic, exhaustive, and compelling product experiences across all sales channels:
Contextualised and enriched product catalogs, tailored for different markets and channels, can be spread across all relevant channels, be they web shops, print materials, mobile apps, online sales channels, or others. PIM capabilities allow the business to deliver on customer expectations.
Get in touch with us and we’ll be happy to have a conversation with you about how we can help you to leverage the power of a PIM to create a truly compelling product experience.
If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data
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]]>The post PIM implementation: top 5 Frequently Asked Questions appeared first on Start with Data.
]]>Retailers, distributors, brands and manufacturers are adapting their business models and infrastructures to respond to an accelerated demand for e-commerce-powered purchasing. As more and more of these companies search for information about PIM and PIM solutions, we’ve looked at 5 of the most frequent questions asked, about a PIM implementation to give them a commercial edge.
Generally, businesses use an ERP system like SAP to store and manage their product or service information. It’s perfectly feasible to use an ERP and a PIM in conjunction, although to do so optimally, it is key to do a discovery and gap analysis to examine how product data is currently managed and how the PIM solution will enhance that management in future. That not only involves the data itself, but also all the procedures, processes, and workflows surrounding its use.
The volume and complexity of product information is rising exponentially, arriving in various formats and from various sources. Suppliers provide product data in varying formats and variable quality and consistency, so to optimise onboarding, a PIM implementation offers a series of AI-powered tools and processes to normalise, standardise and quality-check this information. It also permits suppliers to use an onboarding portal, hence eliminating the need for constant queries about missing or incorrect information. With use of APIs and Enterprise Service Bus (ESB), it will become much easier to access data from other sources, centralise it in a PIM, and guarantee it surpasses the desired quality threshold.
Product data migration involves more than simply transferring source data to a new PIM system. Most data migration projects need a series of complex procedures for data validation, cleansing and transformation. Therefore, the data migration project team must possess forensic expertise in both source and target systems, as a well as detailed knowledge of the current (and desired) state of the data in question. Basically, that means:
Data migration is a complex and critically important component of a PIM implementation. Get it right and you hit the ground running after go-live. Get it wrong, and problems with your product information will stack up.
Many PIM implementation projects do not come to fruition, or end up causing more problems than creating solutions, because the right people were not involved or due to a lack of broad organisation will. The key factors are:
Depending on the size of the organisation and the complexity of the solution required, cost varies. Remember, though, that PIM is an investment and not an expense. Use our PIM pricing calculator to get an idea of the cost for your specific project.
Common variables involved in PIM implementation projects include:
If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data
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]]>The post Why MDM projects fail – Data Management problems and solutions appeared first on Start with Data.
]]>Large numbers of companies encompassing multiple industries are now recognising how important product data is as an asset at strategic level. Clearly no longer driven by IT alone, the impetus behind a master data management project brings CDOs, CMOs, and other Digital Marketing roles into play.
Nevertheless, many initiatives for MDM end up failing to generate anywhere near what they set out to achieve. Below, we examine six of the most common reasons why MDM projects fail, and some best practices to avoid those risks.
A recent study by Stibo Systems and the Aberdeen Group noted that 45% of businesses are unable to locate their master data effectively. That means serious issues affecting business impact. When we pinpoint the reasons, the broad challenges connected to master data management are:
Siloed data – In and outside the organisation, multiple versions of data sources are used, as product data are maintained in several unconnected (and usually legacy) systems. The inevitable results are duplicates, data errors and organisational inefficiency.
Poor (or absent) data governance: data governance is next to impossible without a centralised hub. Not only does this absence prevent your organisation from fully meeting regulatory compliance and safety regulations, but it engenders a ‘free-for-all’ mentality when it comes to accessing, altering the nature of and storing this information.
Quality: Product information uses a lot of unstructured data. If you use inconsistent, incomplete, duplicated or erroneous data, it can cause problems throughout the business, including lack of compliance with key sales channels and marketplaces.
All of which leads to a LOSS OF TRUST: lacking the information to know which elements of your data are outdated or incorrect. Thus, throughout the lifecycle of products, traceability is low.
So, what are the solutions? Simply reverse the three problems above:
Ensure there is a unique version of each data point for everything in the company. Then, ensure that the unique version is stored centrally, and easily accessible in a role-determined manner.
The so-called ‘big bang’ approach to an MDM project is fraught with peril. A hurriedly planned transformation programme with enormous implications runs a high risk of failure in the execution phase – complex and critical issues inevitably arise and if these have not been addressed, the outcome is going to create more problems than n it solves. Cutting corners, going over budget, and failing to fulfil scheduled timelines, could even lead to a de-scoping of the project simply to deliver something.
However, the ‘big bang’ doesn’t happen in isolation. Its prevalence is usually the consequence of a series of other factors.
Failure to enlist strong, C-level executive sponsorship will stymie the progress of a master data management project. MDM implementation must have consistent funding and support in place before the project starts in earnest. An executive with both a desire to champion the project as well as the power to make decisions provides that level of support and leadership. Of course, master data governance (more of which later) helps considerably, but a capacity and willingness to intervene is required, to ensure incidents are properly addressed and the project continues to move forward on expected timelines.
Furthermore, executive sponsorship is critical in two key areas:
Gartner, the renowned analyst firm, has noted that around 90% of businesses fail when first attempting to implement and maintain an MDM project. The main reason for this is a substandard data governance framework – namely, a lack of clarity in documenting processes alongside an inconsistent or incomplete set of rules-based enforcements.
If the underlying purpose of master data governance is to put in place a rigorous set of checks and balances, that framework must be overseen by a ‘star committee’ of key stakeholders with a vested interest in ensuring the MDM project succeeds and achieves all its predetermined goals
At the discovery stage (and throughout), the master data management project requires input from multiple decision-makers, SME’s, stakeholders and users. Nowhere is this more important than in the integration of the new MDM system with existing systems.
For the MDM project to successfully integrate with legacy systems, the partnership of consultants and solution providers need to discover and act on key information about these systems:
In many cases, the answer could well be “we don’t exactly know.” Hence, what appear to be obvious good practices may be absent or unknown, creating a need for further clarification and imposition of rules (integrated into a data governance initiative).
Linked to the above, MDM projects frequently go off track due to a failure to create sufficiently robust validation mechanisms for storing and disseminating master data. The consequences are inconsistent, incomplete and multiple versions of the same data – in a nutshell, bad quality data. Controls and automated validation protocols are the basis which ensures accurate, up to date and high-quality master data.
At the end of the day, master data management is about business value. Integrating the best technological solution is all well and good, but without the requisite changes in business processes and workflows, it’s unlikely the project will result in optimal practices.
This is where external expertise is critical in guiding the organisation towards the realignment and restructuring of processes and workflows required to support successful MDM project implementation.
We have looked at the most common pitfalls in MDM projects as well as the impact they can have. We’ve also made brief mention of how to address these problems. But we can also outline the bases for best practices as a ‘philosophical’ underpinning for successful master data management project implementation.
They need time from the organisation’s SMEs because if there are several systems to deal with, critical information is locked into the project’s decision-making and action needs, be they business or technology oriented. SMEs possess intellectual property, and this is critical to the successful delivery of an MDM project.
The master data model used will mean significant differences in business operations. in today’s fast-moving digital environment, any MDM solution must be agile and adapt to the changes in complex systems. The technological tools certainly exist – the power of machine learning, AI and easily automatable processes and tasks are widely available nowadays. Therefore, any ambiguous or inactive master data model doesn’t solve the current problems it was intended to address. A fundamental tenet of an MDM project is, therefore, to define the layers of the data model.
That means addressing and developing the following:
An absolute must is to set the standard for master data in your organisation. It is also a challenging factor. Whatever standard set for master data should be fully aligned with and adaptable to all data types across the organisation. This standard should take place at the project planning stage.
Master data is amalgamated from various sources throughout the organisation. Both the meaning and use of that information must be closely defined, given that what looks like the same information from one department may well be interpreted in a different way in another.
Data governance is absolutely key to developing enterprise data definitions which align with master data.
Different businesses are at different levels of maturity when it comes to data governance frameworks. For those organisations whose data maturity level is behind the curve, the whole issue of data governance often appears to be an overcomplex and excessively time-consuming exercise. But, in trying to establish a common set of master data, it is a question of putting the business horse in front of the technological cart, and not the other way round!
Even if definitive data models and standards are in place, implementing an MDM project has further complexities. Governance is a vital element in terms of establishing robust policies and business rules to address these complexities. Essentially, it gives you a clear overview of all data operations throughout the process.
However, data governance should not be seen as a one-time data cleansing initiative. It needs to be an ongoing set of protocols and processes in identifying, measuring, and rectifying data quality issues in not only the source system, but also any other systems with which it interacts.
A data governance framework is certainly not something to be tacked on at the end of an MDM implementation project. It should be ideated and developed to a greater or lesser degree during the planning stage (depending, obviously, on the organisation’s level of data management maturity). This is an initiative with massive implications for all areas of the company, as it will to a large extent determine the viability of the overall business aims of the project.
So, inadequate governance means bad data quality, which, in turn, creates the risk of long-term problems for business operations. That is why data stewardship is fundamentally important for maintaining data quality.
The key steps to consider are:
Organizations have acknowledged the benefits of bringing together all of their data from all of their disparate systems to maximize their data-driven problem-solving potential, identify new business opportunities, and increase the accuracy of machine learning models. This single view is not only used to power more accurate data analysis, but is also flexible enough to drive your operational business process.
If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data
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]]>The post Golden Record Management appeared first on Start with Data.
]]>The term ‘Golden Record’ refers to the most accurate, complete, and comprehensive representation of any master data domain and indicates that any data labelled as such can be safely used for any application across the enterprise. ‘The best version of the truth’ and ‘The single source of truth’ are two other common terms for this data standard.
When we refer to this golden record in relation to a master data management system, we are making the bold and explicit statement that the data labelled as such is safe, usable, and a guaranteed asset for all business operations and initiatives.
In the broad spread of data management as a discipline, ‘The Golden Record’ is a foundational principle. As in other areas like product information management, when it comes to the golden source for Master Data Management (MDM), the term identifies and describes the single version of the truth – that is, where ‘truth’ is understood as the single and unique data item which is trusted as both accurate and correct. When we compile master database tables from various data sources, we frequently find common recurring problems like duplicate data records, incomplete (and/or missing) values within a data record, and data sets with substandard quality levels. The purpose of the golden record is to resolve these problems by correcting or deduplicating, inserting values when a value is missing from a data field, and generally bringing data quality up to a previously determined standard. Thus, the golden record is what your organisation assumes to be the highest quality possible at a given moment. That gives users throughout the organisation the security that the data can safely be deployed for business purposes.
The most disruptive problems for the performance of product-centric organisations are duplicated records. If many of these exist in a master data management environment, it seriously affects its usefulness.
We can see a simple example of how data duplication can complicate even the simplest of information for a vendor. We could use an individual consumer – bad enough – but let’s assume it’s a B2B relationship, where the volume of purchases is large, creating a bigger disruptive impact.
An industrial distributor may record the customer as ‘S Jones Tooling’ when sending out a receipt, while the same distributor’s in-store CRM record shows a customer labelled ‘S.Jones Tooling’ in the name field. Separately ‘Samuel Jones Tooling’ sets up an account with the distributor’s ecommerce platform. So, we have the same customer with three different versions
When merging these two databases, ‘S Jones Tooling’ and ‘Samuel Jones Tooling’ will automatically become two discrete data records. The consequent problems are clear when accessing what appear to be two separate accounts. The distributor knows that this is one company, and its systems should reflect this – otherwise, CRM, marketing, sales records, and order tracking (amongst other departments) will see confusion and disruption at best, customer abandonment at worst.
Master Data Management systems usually manage data across multiple domains. Information on employees, customers, financials, locations, and vendors are typical, generic MDM domains. Consequently, when planning and executing an MDM project, the scope is usually very broad and complex, requiring large amounts of resources to implement and maintain.
MDM systems exist to enhance a company’s business operations and bottom line, so use cases often cover several business objectives and functions departments across the organisation. These business objectives include:
So, the golden record is not simply a desirable state for a company’s data – it is an absolute necessity.
Perhaps the biggest factor in implementing a master data management solution is designing workflow protocols to establish the golden record. This is where having a solid and consensualised framework of data governance, stewardship and ownership is critical.
For all data sources, what needs to be established is clarity around the fields where the data sources appear to be more reliable. That develops the criteria for deciding which system establishes primacy in determining whether to populate other MDM domains. Using our example from earlier, if you have a CRM system which captures the customer’s name, and a delivery system which also lists that name, the decision is whether one or the other of these systems tend to list customer information more correctly (or at least, with fewer discrepancies).
When creating and maintaining the golden record, the operations of matching and merging records are common, so if you have two very similar records, what is the procedure for determining which of the two is the correct one?
Of course, life cannot be so simple where the golden record is concerned. There are multiple cases where no single source field is the clear ‘winner’ in terms of correctness and reliability. Manual cleansing may be required to determine which record takes precedence. That is where workflow management tools are very useful. Using the pre-established data governance framework, the data steward assigned as responsible is able to judge which field from which record should be used as the right version. The consequent modification to the existing golden record has implications, so there should also be approval mechanisms in place – when a data record is definitively merged, it needs to be signed off by that steward. Decisions made regarding the golden record need both clear lineage and traceability.
Data analytics technology is constantly evolving, and cross-functional solutions are becoming more common. For the purposes of the entire organisation, the golden record is the foundation upon which data quality is built. It informs many parts of your business, especially master data management and metadata management.
At Start with Data, we carry out a targeted three-phase Product Data Quality Assessment to guide your organisation on a prioritised roadmap for process, data governance and technology improvements for master data management.
We use the best data cleansing tools, designed to enhance the accuracy, completeness, relevance, and consistency of your product data so that when it comes to centralising those data sets, you can be sure that you have the best possible golden record.
We pride ourselves on our flexibility and adaptability to clients’ requirements and it may be the case that your organisation will need to access certain parts of our Data Quality consultancy services. Our mission is to serve your needs with our expertise, so that the outcome aligns totally with your brief. Get in touch for a conversation about how we can help you to achieve your golden record.
If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data
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]]>The post Data cleaning techniques for product data appeared first on Start with Data.
]]>If there is a key element of a PIM or MDM implementation project, it is data quality. Without a de facto high-level quality threshold, the project will be plagued with the problems that dirty data compounds. The preventative measures at the discovery phase of the project include a data profiling phase which identifies a range of potential problems. Once substandard data has been identified, the process of how to clean data (also known as cleansing) can take place.
If your business aspires to being data-driven, recognise that you are sitting on a very valuable asset. However, data is a double-edged sword – whereas clean, high-quality data enhances business performance and enables goal achievement, dirty data can cause serious damage, impacting on your reputation, efficiency, finances, operations, and competitiveness.
This includes deleting duplicate/ redundant or irrelevant values from your dataset.
Errors can emerge when measuring or transferring data. These are known as structural errors. They may include typos in the product data (such as names of features, or the same attribute using a different name, mislabelled classes, and inconsistent capitalisation.
An outlier is an observation which is an abnormal distance from other values in a random sample from a dataset. Overall, it is better not to remove outliers until there is a legitimate reason. Removing them may improve performance, but not always.
Missing data can be a tricky issue, especially when using machine learning. Missing data may simply be ignored or removed, as the absence may be an indication of something significant. Therefore, involvement of the relevant subject matter expert is essential.
“‘missingness’ is almost always informative in itself, and you should tell your algorithm if a value was missing”.
Imputing missing values means assuming an approximate value calculated using linear regression or median. Of course, this method has risks, as we cannot be certain that it is the genuine value.
Data types should be uniform across datasets. There are several things to bear in mind when converting data types:
Data mining is the process of pulling valuable insights to inform business decisions and strategy, while data cleaning is the process of removing bad data, organizing the raw data, and making it fit for use. Essentially, data cleaning prepares data for mining, which is when the most valuable information can be deployed from the data set.
Data cleaning steps in data mining are time-consuming, and this has historically created a dilemma for data specialists in terms of having sufficient staff or time to clean the data. However, without high quality, the deployment and insightfulness of those data will certainly be severely compromised by inaccuracy, inconsistency, and other issues.
There is a wide range of data cleaning tools on the market. Whether they are suitable or not for requirements is a question of getting expert advice and doing research. The most commonly used data cleansing tools offer some or all of the following features:
When we come to how to clean data for machine learning, the use of artificial intelligence and machine learning tools for PIM and MDM systems has massive benefits. It allows us to organise product information using large volumes of product metadata, it can analyse data and match it with the statistical rules governing compliance and quality and it can obviously speed up a wide range of processes, saving time and money.
However, machine learning is not ‘intelligent’ as such. It can only function effectively if the ‘raw material’ with which it is working is of a high quality. All users of product data and metadata are entering prices, data assets, fact sheets and parametres of channel, legal and regulatory compliance. If any of these are incorrect, inaccurate, incomplete, or invalid, the machine learning tools are working on a false premise and problems will inevitably emerge. We cannot, therefore, skimp on data cleaning simply because we assume our algorithms will put it all right. The algorithms we use can be powerful, but without the relevant or right data training, a system will most likely fail to provide optimal performance.
Selecting the right data cleansing tool can appear difficult, but if you do research and take the advice of a trusted 3rd-party expert like Start with Data, it ends up being a tremendously effective way of achieving high quality data and ensuring your MDM solution hits the ground running after go-live.
If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data
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]]>The post PIM vs PIM – a solution comparison appeared first on Start with Data.
]]>A PIM platform has the potential to add enormous value to a company’s bottom line. The very first step is choosing the best PIM solution for the precise needs of that company. Manufacturers, distributors, and retailers of all kinds can benefit from better quality product information delivered to all sales channels and can easily scale their offer in volume and reach.
On the other hand, the project executed for PIM implementation will run the risk of failing from/the start if:
Therefore, application of accurate selection criteria is key to selecting the right PIM platform.
The pricing format for purchase, implementation and support need to be transparent up front, as well as being affordable within budget constraints. Most expensive is not necessarily best. Most cloud-based PIM solutions offer flexible pricing options based on the user licence model.
If you are operating with multiple systems like ERP and CRM, you want your PIM solution to offer automatic and complete integration and synchronisation with other internal systems and should be configured to fit seamlessly into your current system infrastructure. Several PIMs offer a suite of ready-built integrations as an add-on purchase.The pricing format for purchase, implementation and support need to be transparent up front, as well as being affordable within budget constraints. Most expensive is not necessarily best. Most cloud-based PIM solutions offer flexible pricing options based on the user licence model.
The majority of PIM software on the market is designed for specific market sectors, such as automotive parts, clothing, or computer components. Other solutions may be cheaper but require configuration or specialist implementation. The balance ultimately is between costs for customisation compared to out-of-the-box industry specificity
Historically, PIM solutions have only really been cost-effective for large, multi-channel enterprises. Fortunately, there are now several PIM systems available for smaller-sized companies. Of course, they may be more limited in terms of functionalities and volume capacity. If scalability is the aim, think strategically about your choice to avoid more upheaval with another data migration and new PIM five years down the line.
PIM solutions can manage physical as well as eCommerce sales platforms. As the two modes have separate requirements for PoS and inventory, it is worth considering the cost-effectiveness of a PIM solution designed purely for eCommerce.
The following overview outlines the main factors to consider when comparing PIM platforms and solutions. We have then chosen ten popular PIM solution providers, providing an easy-to-consult comparison table followed by brief comments about each vendor –capabilities and best use in terms of sector, company type(s) and size.
Here’s a list of the criteria, with a brief outline of why it matters for your assessment:
A PIM platform may simply be for Product Information Management but also might serve for Master Data Management, CRM, and data asset management. The nature of the platform will impact on the pricing, complexity, and functional extent of the solution.
PIM platforms based on the SaaS model mean easier scaling of the product offering. Additionally, they are available anywhere in the world, and vendors usually offer iterative improvements in the range of functionalities.
Whether a multi-tenant PIM is one where multiple users can share a single instance of the platform. That means seamless cycles of development and release but also guarantees that each customer’s data is safe and secure.
A key element of a PIM solution is how well it integrates with other solutions such as ERP, web shops and product catalog builders. With API-enabled connectors, this integration is rapid and easy.
Freeing up suppliers and other external partners, to onboard their product data enhances agility and provides a seamless process. It ensures a fixed format for data imports from the entire range of suppliers as well as validation rules automatically applied to guarantee data quality. Underpinning these functionalities should be a secure and detailed permissions functionality.
Nowadays, it is common for PIM system vendors to offer access to a marketplace of apps which add onto the PIM system and connect it to the IT ecosystem in the organisation. So, it clearly adds value if the provider has a range of these tools – Syndication, marketing campaign management, connectors to other systems.
When we talk about product enrichment on a PIM platform, a digital asset management functionality is the enabler. In the present eCommerce environment, the very minimum for efficient product data management are functionalities which permit labelling, downloading, or carrying out other formatting on rich media assets.
A PIM platform is most effective when its use and applications are governed by a strong rules-based framework.
Winshuttle/Enterworks PIM solution
Winshuttle’s focus crosses industries, with many of its customers in retail and manufacturing. Its Enterworks PIM platform is closing the onboarding and creation gap with other providers by launching its enhanced Smart Template Pro. The Enterworks PIM solution is a good option for big organizations which want a multidomain solution to support both digital and physical product data.
inRiver versatility is embodied in its design for both B2B and B2C retailers. It’s a good fit for companies with high volumes of product data across many channels,
Reference customers praise Akeneo for its user-friendliness, particularly its hierarchy management. Integrations are also regarded as one of its strongest elements. Like many other providers in our chart, governance and process support isn’t notable, but Its partnership with syndication platform specialist, Productsup, is addressing that area of its offering. Akeneo is a good option for those companies wanting a PIM for marketers with minimal IT support required.
In its design, Riversand’s PIM platform has enterprise-level brands in mind. With the launch of its Ascend platform, it is focusing on midmarket, high-growth companies who want an agility in their PIM platform. Core activities covered include retail, manufacturing, and distribution, with particular emphasis on the automotive, food services, and healthcare sectors.
Salsify is a good fit for retailers operating with multiple user requirements. It has flexible role-based permissions and a range of tools for effective collaboration. It also boasts an audit history capability, making it an ideal alternative for large teams using product data which needs constant changes. Unlike many other providers, Salsify has strong governance and process support and notably speedy data enrichment tools.
Pimcore PIM solution
Pimcore is available for free as an open-source Community edition (with limited functionality), as well as a subscription-based payment model for enterprises. Pimcore’s platform is underpinned by API-driven software architecture. And allows businesses to manage product information, digital assets, digital commerce, and web content in a consolidated platform. It is ideal for software integrators, and enterprises wanting to have a consolidated management of product information, digital assets, and master data.
Pimberly PIM solution
Pimberly’s platform is particularly suitable for companies managing large and complex product catalogs. Reference customers praise its speed in scaling SKU volumes rapidly. Additionally, ease of deployment is mentioned in many reviews.
Contentserv PIM solution
Contentserv’s platform is aimed primarily at B2B businesses, with its sizeable customer base being midmarket technical brand manufacturers, distributors, and retailers. It is a strong performer in product data enrichment, multilingual support, and search optimisation.
Informatica PIM solution
The Informatica platform is regarded as being good for B2C retailers wanting to enhance product data accuracy and simplify the supplier onboarding process. Capabilities include configurable user interfaces, digital asset management, automated data processing, a self-service portal and quality checking.
Your PIM needs to not only store product data uniformly but unify it as well. Work closely with your consultancy partner and chosen service vendor to discuss how unification (such as cleansing) can take place during the data collection and data migration. The stakeholders involved need to be clearly identified and the selected PIM system should be able to support this key and sensitive process. The key to establishing durable and consistent rules-based actions lies in the early establishment of a robust and well-thought-out data governance framework. This should be constructed with all key stakeholders, decision-makers and users.
Given the complexity and scope of a PIM implementation project, an enterprise is unlikely to be equipped with the scope of expertise to carry it out alone. This is where the support of a specialist PIM consultancy is invaluable. They work hand in hand with the vendor and the client’s key stakeholders and decision-makers to ensure that the project process is executed with minimal disruption and focused on optimal deliverables.
At Start with Data, we are equipped to offer that consultancy service. Our teams’ breadth of experience and depth of expertise ensure the best possible discovery, execution, and post-project support for everything concerned with your chosen PIM solution. Talk to us about your PIM needs.
If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data
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]]>PIM in eCommerce is designed to make your day-to-day operations as easy, streamlined, and rapid as possible. Choosing the right PIM solution will reduce the time, expense, difficulty, and setbacks often associated with keeping an e-commerce business at the competitive coalface.
PIM in eCommerce is deployable for B2C, D2C and B2B to provide a single hub to collect, organise and enrich product information. It can create bespoke product catalogs and distribute them to an infinite number of eCommerce channels. In a nutshell, a PIM platform enables easier and much faster creation and delivery of top-quality product experiences. The resulting high-level engagement with target consumers means you can outpace (and outperform) market rivals.
We’re all buying much more online nowadays. Whether it’s product components or office equipment, furniture or football shirts, all buyers expect an engaging and comprehensive view of the products they are preparing to spend their budgets on.
Product-oriented companies are operating in an ever more competitive environment, so they need to be able to respond in an agile and strategic way to constantly changing demands, a rush of new and more specialised products and our increasingly heightened expectations of the service offered.
That’s where the benefits of PIM in eCommerce stand out. It can:
Basically, we are talking about a better user experience because product information is better.
How can a product-centric business gain a competitive edge over other companies in its eCommerce environment? Very often, it is simply a matter of providing a greater volume of accurate, up-to-date useful and relevant information (or product data, if we like).
A PIM in eCommerce is designed precisely to ensure this can happen. It can enrich and enhance product data to the point where it provides greater value to your users than they get elsewhere. The dual benefits enhance the user experience from two angles;
The internal user: those team members who can use the platform’s attributes to work faster, with more clarity and across departmental boundaries, all safe in the knowledge that the data they are using is unimpeachably excellent.
The end-user: the consumer, the procurement executive, the business partner. They will all enjoy the ease, rapidity, and product insights they get from accessing fully optimised information. Ultimately, it is not only about making an informed purchasing decision but being so satisfied with the experience that return visits and brand affiliation are highly likely.
The scope covered by a PIM system defines how comprehensive its coverage of information is. The following and more can be stored, accessed and deployed from a central hub and in a form which is guaranteed to be high-quality, up to date, timely and, most importantly, a unique version.
Enriching product data to create useful and relevant information makes your e-commerce tactics become much more successful. The more intelligently laid out and the more comprehensive their descriptions are, the easier it is for target consumers to conduct searches and find the products they want to interact with.
When customers know exactly what they’re buying, they are making a fully informed purchasing decision. Consequently, they are less likely to return products after purchase. The benefit of PIM in eCommerce is a reduction in time spent processing returns and enables growth in a streamlined and upwardly mobile way.
Most companies welcome ways to promote and generate greater volumes of cross sales and up sales. Again, a well-chosen solution for PIM in eCommerce decreases the amount of time required to wrangle unruly product data and concentrate more energy and expertise to sales strategies.
One of the biggest benefits of adding a PIM system is the decrease in product lead times. By allowing for management and editing of all product information at once, go-to-market is speeded up and bottlenecks are eliminated.
PIM in eCommerce enables diverse departments to create, edit and manage product information simultaneously. Even for products on various platforms, a PIM system applies automated updates in real time, to ensure the accuracy and timeliness of item numbers, references, catalogs, SKU data and so on.
Whether they are procurement teams for B2B transactions or the research-comparison-modelled modern retail consumer, purchasers are using an increasingly sophisticated range of different devices and virtual outlets to shop online. Offering a uniform and consistent buying experience across all channels, be it desktop or mobile, Amazon or UK Distribution Ltd., means brand enhancement and an all-round more attractive, easier, and efficient experience for customers.
Optimal use of the right PIM in eCommerce creates such an integrated buying experience that the words ‘reliable’ ‘professional’, and ‘trustworthy’ become synonymous with the brand.
If you are searching for a flexible, scalable and future-proof technology architecture to support your business strategy and outcomes, our PIM consultancy can help you to evaluate and procure the most suitable, future proof and cost-effective PIM solution. We work hand in hand with you to create the most effective strategy to bridge and unify your product data management architecture with your enterprise architecture.
Start with Data is an official partner with several leading PIM and MDM technology providers, including Winshuttle, Enterworks, InRiver, Salsify and Akeneo. Our team of consultants have a wealth of experience in PIM implementation projects across a range of sectors and industries. So, rest assured, you will be in good hands.
Get in touch for a free initial consultation so you can find out more about how our PIM and MDM services can unlock the potential for added value, both for your business and your customers.
If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data
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