Author: Gungun

Enterprises today are surrounded by technology. Every business function, from marketing and supply chain to product management, runs on a digital platform. New tools promise automation, visibility, and efficiency. Yet, despite these heavy investments, many organizations still struggle to make sense of their data, streamline processes, or see the outcomes they expect.

The problem isn’t the lack of technology, it’s the lack of integration.

While installation might give you systems, integration gives you synergy. It’s what turns scattered technology into connected intelligence and allows data to move seamlessly across the business.

Because you can’t buy your way into transformation, you have to integrate your way into it.

The Illusion of Progress

The modern enterprise tech stack often looks impressive on paper. There’s an ERP to manage operations, a CRM for customer relationships, a PIM for product data, and maybe even an analytics platform for insights.

But having technology doesn’t automatically mean having transformation.

The moment these systems start working in silos, the illusion of progress begins.

Teams operate within their respective platforms, each believing they have the most accurate version of truth. Marketing’s numbers differ from sales. Product data doesn’t sync with e-commerce channels. Finance waits days for consolidated reports. And leaders make decisions on partial insights rather than complete information.

This is what happens when organizations install platforms but don’t integrate them.

The tools are there, but the intelligence is not.

Integration: The Real Enabler of Business Intelligence

Integration is not a technical checkbox. It’s a business strategy that determines how effectively your data supports measurable outcomes.

When your systems and data sources are interconnected, they form an ecosystem that continuously communicates, updates, and refines itself. The insights become richer, actions faster, and collaboration stronger.

Here’s what true integration enables:

1. Unified Data Flow – Information travels freely across departments. Sales can instantly access updated inventory data. Marketing can view real-time customer preferences. Everyone operates from the same, consistent dataset.
2. Smarter Decision-Making – Integration allows data from different systems to combine and form insights that are otherwise invisible. You can connect marketing performance with supply chain outcomes or product attributes with customer satisfaction.
3. Operational Agility – Integrated systems reduce duplication, manual reconciliation, and waiting time. Processes become faster and far more predictable.
4. Customer-Centricity – A connected ecosystem lets you understand your customer across touchpoints. Every department contributes to delivering one unified experience.

When your data, processes, and people are connected, your organization becomes intelligent — not just digital.

Why Platforms Alone Don’t Deliver Transformation

Most companies fall into the trap of chasing platforms because they equate new tools with new capabilities. But without a backbone of integration, even the most advanced platform turns into an expensive data silo.

Here’s why relying on platforms alone doesn’t work:

• Each Platform Solves a Fragment, Not the Whole: A PIM might perfect product data, but unless it’s connected to the broader ecosystem of tools and platforms within the organization, it can’t ensure that the same data is reflected across sales and supply chain systems.
• Manual Reconciliation Becomes the Norm: Teams spend time aligning exports, managing duplicate records, or validating mismatched fields — defeating the purpose of automation.
• The Customer Experience Becomes Fragmented: Disconnected systems mean inconsistent messaging, pricing errors, and delays in service — all of which impact customer trust.
• ROI Remains Low: Technology investments fail to deliver measurable outcomes because the insights are trapped within silos.

Installation gives you tools, integration gives you transformation.

From Implementation to Orchestration

Every platform performs a function. But integration orchestrates those functions into harmony.

A well-integrated system ensures that a single action in one platform triggers relevant updates everywhere else — automatically and intelligently.

For instance:

• When new product data enters your PIM, it should flow seamlessly into your e-commerce platform, MDM, and CRM.
• When a customer places an order, the ERP should instantly update inventory, notify logistics, and trigger analytics tracking.
• When the data quality in one source improves, that improvement should cascade across all connected systems.

This is what integration-led orchestration looks like — where systems are not just connected technically but functionally aligned to business goals.

The Strategic Advantage of Integration

Integration gives businesses a level of visibility and control that platforms alone cannot. It moves organizations from being data-rich to being insight-driven.

Here are the strategic advantages integration brings:

1. Accelerated Innovation

 With integrated systems, launching new products, entering new markets, or adding new channels becomes easier. Your existing data and processes scale effortlessly.

• Enhanced Collaboration

 Integration breaks silos between teams. Marketing, sales, and product teams can share data fluidly, improving coordination and speed of execution.

• Improved Governance

 Consistency and traceability improve when every system operates from the same data logic. It strengthens compliance and audit readiness.

• Future-Readiness

 Integrated architectures are easier to evolve. As your business adds new technologies — AI, automation, IoT — they can plug into the ecosystem without disruption.

Integration ensures that transformation isn’t tied to a single platform but is embedded into the company’s DNA.

The Cost of Ignoring Integration

When integration is neglected, the hidden costs multiply. Businesses spend more time fixing errors, cleaning data, and realigning processes. Opportunities get delayed, insights lose context, and customer trust erodes.

Inconsistent product information across channels can affect brand credibility. Inaccurate reporting can skew strategy. And the longer integration is postponed, the harder it becomes to implement later.

Ignoring integration is like building a high-tech skyscraper on a weak foundation — impressive from the outside, unstable from within.

Building Integration Into Your Data Strategy

Integration doesn’t have to be overwhelming. It starts with small, deliberate steps:

1. Define Your Core Systems – Identify the systems that hold the most critical data — your PIM, MDM, ERP, CRM, etc.
2. Map the Data Flow – Understand how data should move between these systems and where the breaks currently exist.
3. Establish Governance Rules – Standardize definitions, ownership, and quality parameters for data.
4. Adopt Middleware or Integration Layers – Use APIs or cloud-based connectors to ensure systems can talk to each other seamlessly.
5. Measure, Monitor, and Evolve – Treat integration as a living process. As new tools and data sources emerge, continue to align them within your ecosystem.

Integration isn’t a one-time task; it’s an evolving framework that grows as your business scales.

Conclusion: Integration Is the Real Transformation

Transformation doesn’t come from how many platforms you’ve implemented — it comes from how intelligently they work together.

When systems connect, data flows freely, decisions align faster, and experiences become seamless. Integration bridges the gap between technology and business outcomes — turning tools into enablers and data into intelligence.

In a digital world overflowing with platforms, integration is what creates purpose.

 Because installation builds infrastructure — integration builds intelligence.

Let’s be honest: when your company fixes its focus on AI, it’s natural to get thrilled about the forecasts, algorithms, and appealing dashboards. However, what no one discusses with you directly is that almost 80% of the work in any AI project is irrelevant to artificial intelligence itself. It’s all about your data, specifically making it ready.

And if your data isn’t ready, your AI initiative won’t last long. AI adoption is no longer just a futuristic goal but a competitive necessity in the modern digital landscape. Whether you’re forecasting demand, automating customer service, or detecting anomalies, success does not rely only on powerful models but on one significant groundwork, data readiness.

But what exactly does the term “data-ready” indicate? Why is data readiness so crucial, and what measures can you take to assess your business’s position??

This blog clarifies everything. You’ll understand what data readiness is, what pillars support it, the consequences of skipping it, and how you can evaluate your organisation’s readiness to fuel reliable, scalable AI.

What Does Data Readiness Refer To?

The groundwork of “data readiness” refers to how equipped your data is in terms of its application in AI and machine learning models. It’s about having data that is not only available but also accurate, structured, governed, and accessible, which indicates that it’s ready to support precise and impactful AI outcomes.

The assumption is that if you have loads of data, you’re ready to move. However, having raw data doesn’t necessarily mean having AI-ready data. Consider it this way:

• Raw data is similar to crude oil: Precious but non-functional until refined.
• AI-ready data is the refined fuel: Organised, consistent, and all set to drive your AI engine.

Also, don’t mix up data availability with data usability. Just because you have data accessibility doesn’t mean it’s free from duplication, is in the right format, or is aligned across systems.

Another key factor is that data readiness is not just a matter of technology but also an organisational alignment that includes the people, policies, and processes under which data is managed, maintained, and accessed. Even the best data infrastructure can fall behind without this structure.

Why Data Readiness is Vital for AI Accomplishment

Consider spending time and effort on developing an AI solution, only to discover that your data is inadequate, unrelated, or inconsistent. It happens more frequently than you’d imagine and is the reason behind the failure of so many AI projects before they ever deliver value. Your incomplete data can directly impact:

• Training accuracy: Your model learns from the data you feed it, and if that is flawed, your predictions will also be flawed.
• Model bias: Unfinished or unbalanced datasets can unintentionally strengthen damaging biases.
• Deployment timelines: Every interruption in fixing data issues pushes back your go-live date, adding more expenses.

On the other hand, with ready data, you boost development, reduce rework, and enhance your AI’s performance from day one. So, if you’re serious about AI, investing in data readiness is no longer an option but a strategy.

The Significant Factors of Data Readiness

Your organisation has to aim for five foundational pillars to become entirely AI-ready. Let’s dig in.

1. Data Quality

The quality of your AI reflects the exact data it learns from. You must ensure:

• Accuracy: Is the data precise and consistent?
• Completeness: Are you taking the complete scenario or just parts of it?
• Consistency: Are formats, values, and logic standardised across systems?

Before feeding data into any AI model, it’s essential to handle missing values, identify outliers, and eliminate duplication, as these are non-negotiable steps.

2. Data Governance

AI projects demand reliability, transparency, and authority, where a strong data governance ensures:

• Clear ownership of datasets
• Defined access controls and user permissions
• Reliable data lineage to help you recognize the source of your data

And if your data includes personal or sensitive information, you must also align with regulatory compliance, whether it’s GDPR, HIPAA, or industry-specific standards. Non-compliance can result in shutting down your AI project before it starts.

3. Data Integration

AI succeeds in holistic insights, which often demand a combined dataset from various sources, including ERP, cloud applications, CRM, or even IoT devices.

You won’t get the full picture if your data lives in silos. The integration allows for:

• Consolidated views across product, customer, or active data
• Removal of duplicate entries
• Seamless data flow between systems

Getting this right enables richer, more precise AI outcomes.

4. Metadata & Context

Contextless data has no meaning. AI demands understanding much more than just value.Metadata such as descriptions, labels, timestamps, and tags helps your models understand the data properly. 

It also supports explainability, which is important for regulated industries where AI decisions must be correct. Never ignore the role of business context, as it’s something that connects your data to practical scenarios and makes AI outputs actionable.

5. Infrastructure & Accessibility

Lastly, your data needs to be stored and delivered through a setup that’s both scalable and real-time ready. This involves:

• Cloud-native storage
• Data pipelines that feed transparent, efficient data into AI tools
• MLOps frameworks that standardise data movement from collection to modelling to deployment

Even the best models will underperform if your systems fail to deliver accurate data to the right people at the right time.

Assessing Your Organisation’s Data Readiness

So, where do you stand?

Here are a few significant questions to help evaluate your readiness:

• Do you have transparent, reliable, and well-documented data?
• Do you have data ownership and transparent data access policies?
• Are your systems interlinked, or is your data stuck in silos?

While you don’t have to overhaul everything at a time, a phased strategy helps. Begin with high-impact datasets, enhance visibility and governance, and scale from there.

Think of applying a data maturity model or a readiness checklist to support your assessment, even an informal one, that can focus on lacks and opportunities.

Common Mistakes to Avoid

When organisations flash into AI, a few common mistakes appear in a loop. Avoid these, and you’ll be leading the competition:

• Skipping groundwork: Jumping directly into model building before sorting out your data
• Underestimating the effort: Thinking a one-time ETL script is sufficient
• Lack of collaboration: When data teams and business users don’t match, objectives and insights lack coordination
• Short-term thinking: Treating AI as a project, not a skill that requires lasting data investment

Conclusion

The fact is AI is only as smart as your data allows it to be. If your data is unorganised, distributed, or inaccessible, your AI won’t deliver the expected value. But if your data is transparent, relevant, and context-rich, you set the stage for intelligent systems that are helpful.

Data readiness isn’t a checkbox to tick but a strategic skill. It demands communication between processes, people, and platforms. And it pays off at every stage of your AI journey. So what’s your next move?

Start by taking stock, audit your key data sources, notice gaps in ownership or structure, and commit to refining one layer at a time. Want to understand your organisation’s position on the data readiness scale? Contact our data strategy experts for a free consultation.

Today’s B2B buyers expect the same thing across every channel: immediate access to complete, accurate, and actionable product information.

25% of them now prefer a fully digital, rep-free experience—and that number is rising fast. Even those who still buy through reps or distributors do most of their research online. If they can’t find what they need, they don’t wait. They choose someone else.

That shift has major implications for product data readiness—especially in channel-driven businesses like manufacturing and distribution.

Here’s what happens when the data isn’t there:

· Distributors delay listings or stop selling products altogether

· Channel partners lose trust and prioritize suppliers with better content

· Buyers drop out mid-funnel due to missing or inconsistent specs

· Sales teams spend more time cleaning up than selling

· Launches slip, opportunities close late, and high-potential SKUs underperform

One industry report estimated that poor product data costs B2B companies up to 15% of annual revenue—not through IT inefficiency, but through missed sales, slower growth, and competitive leakage. In industrial sectors, the number is likely higher.

And while many companies have invested in PIM or MDM platforms, those systems were built to manage data—not to prepare it. So the real work of standardizing, validating, and syndicating still falls to overworked teams using Excel and manual workflows.

That’s not sustainable.

It usually starts with urgency. A missed launch window. A report that doesn’t add up. A customer touchpoint that goes sideways, because the data behind it wasn’t there, wasn’t right, or wasn’t trusted.

From there, the scramble begins. Teams look to the system: maybe it’s outdated, misconfigured, or just not “smart” enough. A new platform feels like the fix. Something faster. Sleeker. More powerful.

But here’s what we’ve seen, again and again: The system might be new. But the struggles are the same.

Manual workarounds. Inconsistent content. Channel delays. Reporting gaps. Why?

Because the real issue wasn’t the platform, it was what you asked that platform to manage.

Bad data doesn’t magically become good data in a new interface. And most systems aren’t designed to be business-ready from the outset.

That’s why the companies seeing real transformation today aren’t just upgrading tech. They’re upgrading their data strategy first.

Data Is Not a Byproduct—It’s the Core

In most companies, data is treated like an output. Something that shows up once the system is in place.

But the reality is the opposite: Data is the input. It’s what drives performance, accuracy, trust, and scale.

You don’t need another dashboard to tell you something’s off. You need to fix what’s underneath it. When your product data is fragmented, incomplete, or out of sync, no amount of interface design will solve the root problem.

You need structure. You need standards. You need a model that actually reflects how the business works. And that doesn’t start with technology. It starts with intent.

Why System-Led Transformation Keeps Falling Short

Most organisations don’t struggle because they chose the wrong tool. They struggle because the tool has become the strategy.

When transformation efforts centre on software decisions, it’s easy to lose sight of what actually drives the business forward: product launches that arrive on time, clean reports that don’t require revision, and content that flows seamlessly from source to shelf without rework.

However, when the focus is purely on implementation, without rethinking the shape, structure, and purpose of the data underlying it, those outcomes remain just out of reach.

The real issue isn’t platform performance. It’s data performance.

Because systems only do what the data allows them to do. If the data is inconsistent, incomplete, or misaligned, even the best tools won’t deliver the value they promised.

So the better question isn’t “Which platform should we invest in?” It’s “What does our data need to deliver, and what’s stopping it today?”

The Shift: From Tools-First to Outcomes-First

Here’s what the new playbook looks like:

1. Start with the outcomes. What do you need to deliver, automate, measure, or improve?
2. Work backwards to the data. What attributes, hierarchies, and relationships power those outcomes?
3. Define the rules. What does “good” data look like for your products, your channels, your buyers?
4. Build systems that support that, not just systems that check the box.

It means prioritising speed to market over technical complexity. It means improving channel confidence by giving partners the right content the first time. It means letting sales and marketing work with data they trust, without calling in the data team every time.

And yes, it means pushing back when someone suggests that buying a new tool is the whole solution.

Business First. Data Led. Automation Driven.

That’s the model. And it’s working.

Instead of racing into the next replatform, more companies are stepping back and asking: Is our data actually fit for purpose?

• Can it support channel-specific syndication?
• Can it scale as we grow into new markets or categories?
• Can it adapt when buyers change how and where they engage?

If the answer is no, then you don’t need a new system. You need a new approach.

One that gets the fundamentals right. One that builds trust from the inside out. One that aligns your data with the goals that actually matter.

Because it’s not about adding another platform to your stack. It’s about making your entire stack work harder, by making the data smarter.

Conclusion: The Real Transformation Starts With the Data

Digital transformation isn’t just about tools. It’s about outcomes.

And outcomes come from data that’s complete, structured, and ready to move, not just stored somewhere new.

So before you greenlight that next system upgrade, take a beat. Ask yourself: Is our data ready to support the future we’re building toward?

If the answer is no, the next best investment isn’t the system. It’s the strategy that gets your data right first.

Because once your foundation is solid, the rest of the transformation doesn’t just get easier. It actually works.

How about we take the first step towards ensuring our data is right for our systems? We at Thoughtspark can do this for you, all we ask is for you to connect with us!

Effective product information management (PIM) is essential for firms dealing with large catalogs. Maintaining data consistency and upkeep across internal systems and sales channels can be challenging, often leading to inefficiencies and errors. However, these difficulties can be addressed with a robust Product Information Management (PIM) system.

A strong PIM system can streamline product information management, ensuring data consistency and accuracy across multiple platforms. For instance, companies implementing PIM systems report up to a 40% increase in data accuracy and a 30% reduction in time spent managing product data.

PIM systems facilitate the creation of compelling product narratives by integrating data from various sources and presenting it cohesively across different customer touchpoints.

The adoption of PIM systems is growing across various categories, including retail, manufacturing, and distribution. To fully leverage the benefits of PIM, businesses must adhere to best practices such as ensuring data completeness, maintaining data quality, and regular system audits. Adhering to these guidelines can help businesses unlock the full potential of their PIM systems, resulting in better product stories and improved customer engagement.

PIM Best Practices To Follow

PIM needs to be viewed from three main perspectives:

• A technical platform
• Manner of working
• Business enabler

This is in addition to delving deeply into the scope, business needs, the impact of implementation on vendors, partners, and other external organizations, and how it cascades to the end customer. Businesses risk becoming lost if they overlook any one of these viewpoints. Product Information Management is a strategic choice that requires financial commitment to adhere to the growth and progress concept of continuous improvement.

1. Establish your requirements and implementation plans

If you intend to alter how you handle your product information, present a compelling business case, raise awareness of the need for improvement, and secure funding and resources to support the change.

Create a list of PIM criteria, such as the following, before thinking about PIM software:

• Operating on the cloud
• Providing mass editing capabilities
• Real collaboration
• Robust digital asset management system
• Using AI and machine learning

2. Define roles and responsibilities

Working together on product content directly within the PIM system can greatly accelerate operations and remove content issues. Depending on your company’s needs, the creation of product content may benefit from the involvement of the marketing department, eCommerce management, or even outside partners.

Because of this, you must select a PIM system that allows you to create many roles with varying levels of access without taking unneeded risks. These consist of the read-only, admin, product manager, and publisher access levels, each with default rights.

3. Standardizing Product Data with a Centralized PIM System

Businesses can streamline and standardize data management by establishing a single, centralized repository for product information. This approach provides a unified view for searching, updating, and publishing product material across multiple channels. Such a system allows the creation of validation rules, ensuring a uniform, industry-standard format for product attributes and categories.

Moreover, using a centralized PIM system helps eliminate the fragmentation of product data across various tools and platforms. According to Ventana Research, 56% of businesses maintain more than five systems holding product data, often resulting in inconsistent data formats and disjointed metadata. A streamlined approach minimizes human error and optimizes time to market, offering a competitive edge in product management and enhancing cross-sell and upsell potential.

4. Data integration is crucial

The majority of businesses utilize more than five systems that hold product data. They must all be integrated to create a single, unified set of data. Given these conditions, it is not unexpected that one of the main justifications for centrally handling product material in a PIM system is data integration.

The greatest PIM supplier currently offers dozens of connectors that are easily accessible. Sources of incoming data may include:

• ERP system
• Excel files/Spreadsheets
• Custom-built systems and platforms

5. Enhancing customer experience with PIM

By integrating PIM capabilities, businesses can significantly enhance customer experience. A B2B Marketing/Earnest poll revealed that 83% of participants would recommend a company to others after a positive experience, and 96% of respondents stated customer experience would impact whether they would make another purchase. With a PIM platform, companies can develop and manage a comprehensive body of material for each product at various customer journey stages. This facilitates multichannel sales and addresses potential obstacles, allowing businesses to connect with customers directly and ensure accurate and compelling product information. By prioritizing product information, PIM helps create a more customer-centric experience, aligning with placing the customer at the center of marketing and sales decisions.

6. Fostering Cultural Transformation and Capturing Your PIM Vision

Business evolution lies at the heart of Product Information Management (PIM), which involves scaling, adjusting, and growing the organization, along with making bold decisions like data quality improvement and process revamping. A PIM vision should include initiatives such as modernizing outdated processes, rethinking business procedures, implementing new assortment strategies, and exploring new syndication channels.

Additionally, fostering cultural transformation within the organization is essential. Employees, contractors, and vendors may not be fully aware of the impact of modernizing antiquated processes and improving collaboration on productivity, accuracy, and work speed. Leaders should prioritize ongoing workshops, inductions, and training to encourage understanding and commitment to the organization’s evolving business vision.

Conclusion

Whether you’re a high-volume shop or just starting your online business, implementing these PIM best practices can give you a competitive edge, increase efficiency, and increase online sales. Understanding that your product knowledge is your genuine “asset,” and sales channels serve as the asset’s ultimate test. To put it another way, knowledge about your products is just as helpful as the actual products. PIM best practices also ensure that.

By Samarth Mehta, GM, India & Head of MDM Solutions, ThoughtSpark

Hello everyone,

I was recently a part of an esteemed panel at the Syndigo Partner Kick Off 2024 in Chicago, where we delved into achieving success with Syndigo at customers. During the discussion, some very interesting questions were raised that I believe are important for all of us to address. Here’s my take on those questions.

What are the factors contributing to Success and Future Business Opportunities?
There are quite a lot of factors involved in winning a deal, but one of the fundamental keys is gaining the trust and confidence of the customer. To achieve this, three key areas that a System Integrator (SI) should focus on are Industry expertise, Domain knowledge (MDM), and Product expertise.

To do these, I think the entire sales process needs to be Consultative, ensuring that you’re talking about the Problems and the Solutions for the customer rather than filling them with MDM fluff goes a long way. It’s important to talk about their industry and vertical, provide relevant examples and talk about how the business would benefit from the overall solution.

Additionally, demonstrating expertise in the product plays a significant role. Leveraging customer data, understanding their unique needs, and customizing a product demo accordingly helps a lot.

Finally, a very basic but crucial thing, being responsive and providing clear, timely communication throughout the sales process, is very important. Syndigo has templates for Scoping & Estimations that can highly standardize proposal building. I believe it is important to respond on time, with clarity & providing the right context.

Why Syndigo? How did Syndigo solve your customer’s business issues?
Flexibility & Rapid Time-to-Value: Syndigo’s standout strength, according to me, is the flexibility & the rapid time-to-value of the Solution which allows businesses to derive tangible benefits without long implementation periods.

Out of the box solutions: With PIM Standard and Professional, Syndigo offers a range of out-of-the-box solutions. These solutions come with various industry-specific capabilities built-in, making them ideal accelerators for large enterprises while also seamlessly fitting smaller enterprises in their current operational state.

Business Rule & Workflow Engine: The powerful Business Rules & Workflow engines help enterprises in configuring process & rules. This capability ensures that businesses can align the system with their unique operational workflows and requirements.

App Development: Enterprises often face unique challenges that require additional UIs or configurations to be built on top of the product. Syndigo’s App Development framework empowers businesses to build end-to-end customer solutions, addressing these specific needs.

In conclusion, Syndigo stands out due to its flexibility, out-of-the-box solutions, robust business rule & workflow engines, and comprehensive app development framework.

How do you handle scenarios when a customer has a requirement or use case that does not confirm or align with the known best practices or patterns?

It is important to understand the customer’s mindset. The customer or their IT team may come from a background of custom software and therefore they believe in certain specific implementation methods.

When the use-cases do not align, the following are my suggestions:

• Propose Alternative Solutions: Explain objectively how a different solution can address the problem.
• Educate the Customer: Present the pros and cons of the solution to help the customer make an informed decision. I often use the Harvey Ball comparison for clarity.
• Include the Syndigo Team: Keeping the Syndigo team informed about the use case and proposed solutions is essential for alignment and collaboration.

What challenges did you face during your implementation journey and how did you help overcome them?

Below is the list of challenges in the order in which I think they are:

Scoping the Project:
I’ve been involved in numerous implementations over the last decade, and I believe many major initiatives succeed or fail before the project even begins. One of the biggest risks lies in the gap between the customer expectations and the defined scope outlined in the Statement of Work (SoW). The best approach is to ensure that project objectives, milestones, and deliverables are clearly defined and communicated right from the project kickoff.

Customer Readiness:
I’ve observed that customers often underestimate the level of commitment required from their end to execute an implementation project successfully. Involvement from both the Business and the IT teams is crucial right from the very start. Sometimes, business stakeholders already have a packed schedule, leading to less productive workshops and communication gaps. I believe it’s the System Integrator’s responsibility to set the right expectations regarding time, deliverables, and other requirements.

Initial Load & Integrations:
This is an area where I commonly see challenges among most customers. As one of my favorite talk show hosts used to say, “How Hard Can It Be?”—right before a disaster strikes. This is exactly the approach I’ve seen IT teams take when loading data into the system and building integrations between different systems. My recommendation is always to engage in serious conversations about these topics early on. The only way to mitigate the risk is to start addressing these challenges as soon as possible.

These are my thoughts on the questions we had discussed during the panel discussion at the KickOff, and I’m glad we had the chance to tackle some of the key challenges and opportunities in the current MDM landscape.

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Samarth Mehta is the General Manager for India and Head of MDM Solutions at ThoughtSpark. Backed by a long-standing Syndigo experience, Product has always been his primary focus and he has been implementing Syndigo projects since 2014. Samarth thoroughly enjoys solving customer problems, constantly sharing and expanding on product expertise and delivering impeccable product demos!

About ThoughtSpark

ThoughtSpark is your go-to enabler in the Syndigo ecosystem, committed to enabling partner-centric sales and go to market strategies, in addition to implementation services. ThoughtSpark walks hand in hand, sharing insights, knowledge, and expertise to fuel its partner’s journey. Our ultimate mission is to enable the growth and unparalleled success of System Integrators across the Syndigo ecosystem.

By Amit Rai, President, ThoughtSpark

January was abuzz with 2023 round-ups, with some very interesting takes on product highlights, business updates. I’m aware I’m a bit late, but let’s attribute it to all the kick-offs I had to attend last month. It has gotten so crazy that we’ve decided to attempt the 2025 kick-offs in December 2024, aiming to hit the ground running next year. Excuses aside, here I am, with my take on what the year meant for the Syndigo PIM / MDM platform.  

In a nutshell, 2023 was another foundational year in the journey of this product. Innovation is at the heart of Syndigo’s strategy, and in more ways than you can imagine, they used the last year to set themselves up for a groundbreaking 2024. Thematically, the main areas of Product investments that I’m gonna cover today are PXM, MDM, AI, and UX. There are too many acronyms. Let’s expand on each and gain deeper insights into these areas. 

First, PXM. There are different schools of thought across the industry. One claims that PIM has now expanded to include Syndication, experiences, and Digital Shelf Analytics while the other calls this combination, PXM. Regardless of what acronym you prefer using, Syndigo, with the breadth of its product portfolio, is there to solve these use cases and be a leading PXM (I am from the second school of thought) player. Not many can claim to offer the entire breadth of solutions and even Syndigo would fall short, if they did not make the experience zing. Hence, the great focus on seamless integration of the offerings to present a one-stop answer to PXM – Syndigo. 

Second, MDM. Syndigo has long played in the MDM space. They’ve been leaders in the Gartner Magic Quadrant. It is one of the few products that does justice to both PIM and MDM. However, with PIM and Syndication being the front-runners, it appears that MDM had taken a back seat in the preceding year. In the latter half of 2023, however, MDM resurfaced as a key focus area. The strategy was to further strengthen the B2B customer master capabilities and at the same time, create a good B2C foundation. B2C customer solution is a key strategic area for 2024 as well, as stated by Simon, the CEO at Syndigo as well as Nikhil, the SVP of Product at Syndigo. As more and more businesses look to create unique Product and Customer experiences, Syndigo is ready to lead the market. In the coming months, they are set to complete their B2C master offering and merge their already industry-leading Digital Shelf Shopper Analytics with B2C to provide a truly unique offering to their customers. 

Third, AI Was there anyone who didn’t get caught up in the buzz and engagement with AI last year? At ThoughtSpark, we use Clickup for task management, Freshsales for CRM, Pitch for presentations, among others. I promise you, there is an AI module in each of these. I also promise you, nobody in my organisation utilises the AI modules in these solutions. I do though. But just to have fun with what it does. Not because any of it impacts my day-to-day work. But Syndigo started leveraging AI a while ago. They already have ML based matching for a couple of years now. However, 2023 was a year they pivoted to strategically leveraging AI. The theme was to foundationally build the solutions with AI at its core to give maximum value to end users – either by simplifying what they do or by automating and solving critical business problems for them – like OpenAI based content generation, Image analytics, Sentiment analysis, Content Moderation, to name a fe. There is now a dedicated AI product & engineering team focused on innovating around AI in this space. Can’t wait to see the robust AI roadmap for 2024 become a reality soon. 

Lastly, the UX. User experience was at the core of every story, every feature, every use case. There was a huge focus on ease of use, simplicity as well as self-serviceability. The user interfaces have become very persona-driven and intuitive based on how each user in their specific role interacts with the solution. Unlike a lot of other light weight solutions that claim simplicity, Syndigo has the ability to solve from very simple use cases to the most complex business scenarios across industry verticals. If you had asked me a year ago, I would have said that an MDM system cannot have simple UIs and definitely cannot be self-serviceable. However, Syndigo has made huge strides in this area. User experience is now very contextual, irrespective of the complexity.  
 
And simplicity remains a continued focus for 2024 as well, as is evident with the announcement of the launch of a lighter version of PIM. I believe, with a light easy PIM complementing their industry leading Syndication and Digital Experience solutions, Syndigo will position itself in a league of its own. Bye Bye Salsifys and Akeneos of the world. 
 
I recently attended the 2024 Syndigo Partner Kick-off event, where the leadership reiterated the strategic focus areas for 2024 – Continuing to lead the PXM market, Leveraging AI as enabler, enhancing customer domain for better consumer experience and focus on usability and UX. Clearly, 2023 was a building base to their long-term strategy and it is exciting to see them continue to double down on the areas they placed their bets on, months ago. These moves will shape up the industry in the coming years, and it is gonna be an interesting space to keep an eye on. Watch Out! 

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Amit Rai is the President at ThoughtSpark. With strong ideas on everything Data, AI and Tech, Amit loves to experiment, analyze, research and learn, thought-leading everyday. With nearly 15 years of experience in the Syndigo ecosystem, it was apt for Amit to do the 2023 Syndigo round-up.

About ThoughtSpark

ThoughtSpark is your go-to enabler in the Syndigo ecosystem, committed to enabling partner-centric sales and go to market strategies, in addition to implementation services. ThoughtSpark walks hand in hand, sharing insights, knowledge, and expertise to fuel its partner’s journey. Our ultimate mission is to enable the growth and unparalleled success of System Integrators across the Syndigo ecosystem.