There has been a lot of hype, confusion and opportunity when it comes to big data. With investments in big data technologies on the rise and predictions stating that big data will grow at a CAGR of 14 percent over the next five years and eventually account for nearly $80 billion by the end of 2020, organizations are constantly searching for ways to maximize their investments.
The very success of your organization may rely on its ability to utilize big data in order to optimize the customer experience. However, the steps needed to make big data useful – capturing, storing, analyzing, visualizing, searching, sharing and transferring – create quite a challenge. Worse, success is heavily dependent on an organization’s ability to use big data quickly and correctly for both strategic and tactical gain.
But using big data requires organizations to revise and upgrade data approaches and processes. In fact, before an organization can truly reap the benefits of big data, it first needs to decide:
- What needs to be analyzed (and why)
- Which data is relevant
- Which models to use
- Whether actions or changes should be taken based on this new understanding
Many organizations have extensive IT landscapes, with numerous systems churning out big data in real time. While organizations strive to maximize the value of their data, the reality is that the sheer velocity and volume of available data hinders their ability to make accurate and timely decisions.
In fact, big data guru Bernard Marr recently said that, at this point, only 0.5 percent of the data we create is ever analyzed and used. He also added that, by some estimates, a 10 percent increase in data accessibility would result in more than $65 million additional net income for a typical Fortune 1000 company.
Master Data Management (MDM) solutions can create a logical starting point for big data analysis by enabling organizations to create a trusted source of critical master data out of data stored in disparate systems across the enterprise. Master data is the high-value, core information that is used to support critical business processes and typically includes information about customers, suppliers, partners, products, materials and employees.
Companies that engage in MDM gain insights through better data accuracy, and the same impetus should apply when tackling big data processing. In fact, data managed by an MDM system could also supplement existing big data streams, which would enable an organization to gain better analysis and more accurate insights. Aberdeen’s 2014 Big Data survey found organizations with MDM outperformed all others across the board. Rather than leaving data siloed in the various technology dashboards and systems, progressive companies now integrate this data as part of their overarching MDM strategy.
This should come as no surprise given that MDM tends to involve human-created data, whereas big data involves machine/sensor-generated data. MDM can support an organization’s big data initiative by supplying business users with data that accurately reflects the current state of the business and provides a strong foundation for analysis.
How does an organization get started with MDM?
The first step is to determine which data are useful and why. Once established, the next step would be to use the single source of consistent, accurate and enriched product and/or customer data generated by the MDM solution to complement the large volumes of unstructured data that exist within today’s big data environment.
For instance, an organization would have the ability to slice and dice its financial (aka transactional) data and enrich that information using facets stored in the MDM system. The granularity provided to big data by MDM enables the company to gain a better understanding of why people bought one product over another and make adjustments as needed.
This was the case for a large luxury retailer as it was unable to conduct market segmentations across the various eyewear brands it was selling. Its analytics were tied to information provided by the purchasing system, and a majority of its brands were purchased from a single company that held a monopoly on glasses worldwide. As a result, brand was irrelevant from a purchasing standpoint, and the associated information was not stored. The retailer’s MDM system had that data, whereas, its analytics did not. By leveraging the refined data captured in its MDM system, the company was in a position to supplement its operational data with structured information and, in turn, improve its analytics.
So while MDM provides structure to big data for the purpose of better analytics, it provides aggregates to MDM to consolidate understanding and be in a better position to use that data appropriately.
For instance, if a product consistently gets poor reviews and comments in social circles, the organization can review the product master and see all the content that is visible to the customer. This allows the business to modify details, test for improvement and share across sales and marketing channels organizations to improve customer and overall brand satisfaction. Similarly, it also helps organizations determine if it is better to cut their losses and discontinue a product and/or brand altogether.
How MDM delivers timely information for insights
Remember, no matter how much data there is, or how fast it needs to move, the aim is to deliver timely information that users can convert into insight. Master Data Management solutions strengthen an organization’s big data efforts in a number of ways by feeding information to the larger effort and by providing:
- A connected source for eCRM, personalization, social commerce and offline activity
- The ability to combine product and customer master data with dynamic information based on analysis of Web and social media data
- The opportunity to one day create a single source of behavioral information that can originate from multiple data sources
- A single data source for all channels
Organizations are exploiting big data for real business gain as the analytics provide a foundation for innovation, competitive positioning, customer experience and productivity.
An example of how MDM and big data could interact is when an existing MDM hub provides the trusted customer data that could help drive an analysis of Web traffic or perhaps identify multi-channel behavior of existing customers, according to Andy Hayler of The Information Difference. He said it is also possible to imagine the opposite direction, with big data analysis throwing up new master data that could be fed into a master data hub.
A 2013 big data study by analyst firm, The Information Difference, found that more than three out of five (67 percent) of 179 US and European survey respondents saw MDM driving big data, rather than the other way around, with just 17 percent seeing big data producing new master data.
Issues affecting capture of full potential of big data
Those organizations that are able to exploit big data and generate change will gain the most, as they understand the opportunity costs of being left behind. But several issues still have to be addressed to capture the full potential of big data. Policies related to privacy, security, intellectual property and even liability need to be addressed. Organizations need to not only put the right talent and technology in place but also structure workflows and incentives to optimize the use of big data. Access to data is critical, and companies will increasingly need to integrate information from multiple data sources, often from third parties.
Without a doubt, Master Data Management is a powerful and fundamental engine for proper big data usage, for data validation and quality improvement. Managing big data correctly can enhance your customer’s journey and boost sales by delivering the right message at the right time, across all channels.
Christophe Marcant is VP of product strategy at Stibo Systems where he is responsible for defining and guiding strategic product initiatives for multi-domain Master Data Management (MDM) and Product Information Management (PIM) solutions. For 10 years as a retailer, he focused on how product information can be leveraged in eCommerce and omni-channel initiatives. Prior to joining Stibo Systems, he led SapientNitro’s PIM practice where he advised clients worldwide as they considered new product information strategies.