Big Data continues to remain a black box term, even as we enter the second half of 2018. There are questions around it such as, how is it collected and what it takes to manage it and make it usable for business. The easiest way to explain this might be in the form of an analogy, something we could relate to our day-to-day lives.

Trading. Each day, fortunes are either made, lost, incrementally increased or decreased. It used to depend primarily on the boldness of the trader and even more so on the mood of the market. When modern technology emerged in the form of predictive analytics, it changed something.

In an instant, trading became a business of mathematical algorithms, custom indicators, integrated beliefs, and more. It became a thing that enabled the conversion of primary data into valuable insights, allowing you to make future projections based on both real-time and historical data. Predictive Analytics mitigated trade risk and ushered – in silence – data as the blood of modern society.

However, data does have its limitations. And predictive models merely predict, forecasting multiple outcomes based on a pattern of inputs. It is access to knowledge and is anything but infallible.

At the end of the day, the market is as human as the traders who make decisions behind these sophisticated, predictive models. It makes them both subject to the irrational.

Is it then truly valid to tout predictive analytics as the only big data trend to look out for in 2018?

An Overlooked Dark Horse in the Big Data Race: Prescriptive Analytics

Contrary to common perception, prescriptive analytics isn’t a specific type of analytics in and of itself. It’s more easily defined as an umbrella term for the several types of analytics utilized to improve decisions. Researchers have called it the goal of all analytics.

It could be used in two ways:

  • Inform the Decision Logic

Prescriptive Analytics provides input to a process. It could be as simple as aggregate analytics – say how much a customer spent on products the last couple of months – or as sophisticated as something that predicts the next best offer, or the degree of discount needed to entice said customer for future purchase.

  • Evolve the Decision Logic

Decision logics are known to become flawed or degrade over time, marking a sizeable chunk of historical data as unusable. But measuring and assessing the effectiveness and ineffectiveness of enterprise decisions could refine, or even redefine the decision logic to become better. It could be as sophisticated as embedded machine learning algorithms using decision logic to automatically decide what content is to be sent to target audiences.

In 2017, a Forrester Report outlined a warning to business enterprises – to stop wasting both money and time on unactionable analytics. With increasing use and value, prescriptive analytics has a significant role to play to drive analytics in today’s era. It is projected to make business decisions faster, enhancing enterprise efficiency and productivity.

The market for prescriptive analytics is expected to grow as well, with an expected value of $1.1 billion by the end of 2019. Elite organizations such as Google, General Electric, Pitney Bowes, and Pop Sugar among others seem to have caught on to this trend. Question is, will you?

Kishalay-Gangopadhyay
Kishalay Gangopadhyay
Senior Director – North America Operations, Mastech InfoTrellis



The second portion of this two-part blog describes PIM, the challenges PIM can address, benefits of the PIM Suite, and the solutions offered.

What PIM Does

PIM is an end-to-end system used to centralize, organize, categorize, synchronize, and enrich product data according to the business rules of the company. PIM cleanses and centralizes information about products to feed consistent, accurate, and current information to multiple distribution channels. PIM creates a single trusted source of product data by integrating siloed knowledge into an enterprise-wide asset.

PIM Overview

MDM PIM Reference Architecture

MDM PIM Reference Architecture

PIM Suite Benefits

The increasingly important role of customers in product decisions is urging manufacturers to improve product planning, achieve a better balance between resources and features, and adjust product delivery based on customer orders. The Product Information Management solution empowers organizations and enables them to maximize product value. Moreover, using this solution, organizations can reduce the space and effort required to maintain, update, and retire product information. PIM’s capabilities maximize product visibility.

The following table illustrates the challenges PIM can address and how PIM can translate product data into competitive advantage.

Benefits of PIM Solution

MDM PIM Solution

The MDM PIM Solution encapsulates the benefits of PIM and remarkably addresses challenges around Product Management in an enterprise. PIM enables manufacturing companies to create a single, up-to-date repository of product information that can be used throughout the organization for strategic business initiatives.

Global Data Synchronization gets to the core of business objectives, by maximizing the effectiveness of trading partner relationships and improving overall supply chain efficiency to increase revenues.

PIM easily adapts to the changing organizational needs like data model evolution, new products, integrations, and more. PIM offers a simple and intuitive object model designed from business objects such as Catalogs, Hierarchies, Items, or Categories. This separation delivers the data model flexibility and ease-of-use that organizations demand, while simultaneously allowing them to take advantage of the latest database technologies.

PIM offers several features helping organizations create data solutions that make intuitive sense to business end-users, including the key personnel responsible for managing master product data.

As an organization’s requirements change and grow, updating or adding product attributes, catalogs, sales channels, or any other business object can be done without the need for intensive development efforts.

PIM Solution Highlights

PIM Solution Highlights

How Mastech InfoTrellis Added Business Value Through PIM

PIM Case Study 1 PIM Case Study 2

Conclusion

PIM provides a comprehensive, out-of-the-box workflow capability that is highly scalable, configurable, and enables faster product creation. PIM enables 360-degree collaboration in the product by bringing all parts of the organization into a single system with the needed checks and balances to help ensure speed, quality, and accountability.

Kishalay-Gangopadhyay
Kishalay Gangopadhyay
Senior Director – North America Operations, Mastech InfoTrellis



Part one of this two-part series describes what the manufacturing sector is all about, business objectives within the manufacturing industry, key business challenges faced in this sector, and why PIM is needed to overcome these challenges.

An Overview

Manufacturing objectives must be aligned with an organization’s strategic business objectives. The following are the baseline manufacturing objectives that augment an organization’s competency, helping meet customer requirements.

Strategic Business Initiatives

Manufacturers produce tons of goods every day, all of which impact consumers either directly or indirectly. The manufacturers need to share product information with distributors and with direct retailers or digital commerce providers on the Internet. Hence, it is inherent that manufacturers’ decision-making processes are intensely data-driven. However, without a proper system in place, there are a few challenges which hinder the ability to make sound decisions.

Key Business Challenges

The following are typical business challenges the manufacturing industry faces, which build a business case for a dedicated PIM system.

product information management in manufacturing

Product Management

Increased Time-to-Market

The time taken to market products from the time of manufacturing increases due to the following reasons:

  • Delayed data integration due to siloed product data received from disparate systems in heterogeneous environments
  • Frequent product innovations to stay competitive
  • Elaborate manual business processes
  • Ineffective product information collaboration

Inconsistent Product Information

The manufacturing industry faces constant flux driven by persistent financial pressure and increasing customer demand for complex and affordable products. Hence, manufacturing companies have to create product information that is rich, accurate, and well-managed, and best fits the given context of a certain location or trading partner, or a prospective customer.

Suppliers in an organization’s ecosystem manage product information for their own purposes using their own codes and version systems. Multiple versions of product images and videos are managed by creative agencies with different naming conventions. All these factors contribute to inconsistent product information.

Supply Chain Management

The following are factors leading to issues in SCM:

  • Multitude of systems to manage information about stores, warehouses, materials, products, vendors, partners, customers, and suppliers
  • Questionable data integrity across omni-channel touch points
  • Integration with multiple marketplaces and webstores
  • Lack of timely notifications and remediation processes
  • Poor visibility in tracking supplier performance, deliverables, cost-effectiveness, and spend analytics
  • Lack of effective and real-time data syndication to partners, customers, suppliers, vendors and global data pools
  • Spectrum-wide integration with Digital Asset Management tools

Product Governance


Kishalay Gangopadhyay
Senior Director – North America Operations, Mastech InfoTrellis