How Master Data Management powers big data decision making.

Transcription

1 decision ready. How Master Data Management powers big data decision making. Building an enterprise architecture that s decision ready.

2 Bringing discipline to big data. The trouble with insight is it doesn t just pop out of data. It takes intelligence and an understanding of the business to identify useful relationships between different types of data and a comprehensive understanding of the content, context, and correlations within it. Most important, it takes a foundation of clean, safe, and connected data to give your analysts the information they need. Without this foundation of data you can trust to be accurate, up-to-date, and de-duplicated, the information suffers, the analysis stutters, and the insights lack authority. This is why Master Data Management (MDM) has become such a crucial layer in the enterprise analytics stack because it prepares your data and identifies critical relationships within it before you invest time in analysis. In fact, by combining interactional data and transactional data into master data profiles, it overcomes one of the oldest challenges in enterprise data management. In short, MDM helps make your data and your analysts decision ready. The traditional analytics environment was characterized by small, stable, and structured datasets. Even then, mastering was a crucial challenge. But in the big data world of unrelenting, unstructured, massive data sets, mastering data is a whole new kind of challenge. One that s impossible to do using manual processes and integrations alone. And certainly one that s impossible to do with old data warehouses that both cost too much to customize and struggle to adapt to changes in the data model. This ebook explains the importance of applying the proven discipline of MDM to the new world of big data and the new analytics potential that this opens up for every discipline in every enterprise. Informatica How Master Data Management powers big data decision making. 2

3 Mastering the challenges of the four Vs.

4 Mastering the challenges of the four Vs. The defining characteristics of big data are the very things that make it so much harder to manage and master. Between different data types, different formats, different sources and different use cases, managing big data means managing complexity. It all comes down to the four Vs: Data volume Mastering by matching duplicates across millions of customer records might take hours when your datasets are small. But when you re dealing with billions of records, it could take you weeks before the data is even ready for analysis. That s an insight lag most businesses can t afford. Data variety When you re mastering across one or two datasets, the challenge is non-trivial but straightforward. But when you re consolidating multiple datasets in multiple structures from multiple sources, it gets infinitely more complex. And it only gets harder if you don t have control over some of those source systems. Data velocity In a world where the growth of data is slow and data tended to be held in static databases, you could afford to spend a few weeks mastering batches of data. But when your operations produce non-stop, real-time, streaming data (like the social media fire hose), your master datasets have to update almost instantaneously. Data veracity The more data and source systems you have, the more inconsistencies and discrepancies you have. Even worse, the instances of poor data multiply and get harder to identify let alone manage. As the veracity of big data decreases, the importance of reliable, mastered data increases. Instead of a Four Vs world, it s more important than ever to invest resources into making your analyst teams and applications decision ready by mastering your data. It s also much harder. But as many big data project leaders have discovered, attempting to analyze a massive data lake without mastering it first is virtually impossible. So while the potential for insight in big data is massive, there needs to be a new generation of Master Data Management to realize all that potential. Informatica How Master Data Management powers big data decision making. 4

5 Leveraging Hadoop to power MDM.

6 Leveraging Hadoop to power MDM. Hadoop dramatically lowered the threshold of viability for big data analytics. With its low-cost, highperformance, and scalable parallel processing, it helped businesses query huge troves of data. So if you re going to attempt to make big data more accurate, reliable, and usable and to make you decision ready it makes sense to leverage the scale of Hadoop s distributed processing to accelerate the discipline of Master Data Management. Bringing MDM deployments into Hadoop is the most effective way to apply the data preparation processes your analysts need to the big data you have. And it changes everything: Accelerated insight The integration, cleansing, and mastering processes that would have taken you weeks to complete take only hours in a Hadoop deployment. Liberated resources The matching run that would have sucked up an entire team for weeks is now almost zero-touch. So those $300K data scientists can skip the data wrangling and focus on building models. Easier integrations Faster MDM means you can add more data sources without the fear that each new source will add days or even weeks of additional data preparation work. Multi-dimensional analyses Effective, Hadoop-based MDM means your analysts and data scientists have a far broader view of all your data dimensions. So the potential for learning what you don t know grows exponentially. In short, it takes less time to integrate, cleanse, master, and store the most authoritative version of your businesscritical data all at a big data scale. With Hadoop-native MDM, you can leverage big data to reliably power your operations in production environments not just offline batch analytics runs. And that makes some important things possible. Informatica How Master Data Management powers big data decision making. 6

7 Three analytics opportunities powered by big data MDM.

8 Hadoop-native MDM not only makes mastering possible at big data scales, it also opens up new opportunities that had been blocked by the Four Vs. Some examples:

9 One Reveal the relationships that really matter. It s really the first step in data analysis because it uncovers and surfaces the relationships between records that would otherwise go undetected. Relationships like these (four examples from a near-infinite list): Persistent and complete customer profiles knowing that George Kessler is also GA Kessler and Mr. G Kessler. Customer and household relationships George resides in the same household as Mary. Customer/product relationships George bought three products from your business online but has never been to a store. Cross-application relationships the George Kessler in your SAP system is the customer your homegrown fulfillment application shipped to and your Oracle financial package billed. When you can establish relationships like these to derive a 360-degree view of any entity you care to study, you can use big data to make better decisions about those entities. This is where the true value of MDM reveals itself. UPMC s personalized patient care UPMC is a world-class healthcare delivery provider that used Informatica s MDM technology to power a more comprehensive, self-service approach to its analytics. By integrating clinical, financial, administrative, and genomic data to give decision-makers a fuller view of patient information, UPMC was able to devise a repeatable, personalized way to produce the best possible results for patients. It s big data but it s mastered big data. Read more about UPMC here. Informatica How Master Data Management powers big data decision making. 9

10 Two Discover new insights from your analyses. Hadoop-native MDM is much faster and less expensive than traditional analytics. That means you can add many more data sources into your analysis and generate insight from richer datasets and profiles. A few examples: Better product recommendations A major insurance company added householding data to its analysis and discovered some significant opportunities, including seeing when a customer s child reaches driving age and making a proactive car insurance offer. Smarter fraud detection A national credit card issuer improved its fraud detection algorithms, feeding a wide range of new datasets from its own data and third-party sources into its machine learning via MDM. The result: less fraud and fewer false triggers that annoy loyal customers. Social media listening Social media is one of the richest sources of consumer insight. But there s so much of it, it s unstructured, and it never stops streaming. Without Hadoop-native MDM, it would be impossible to analyze this rich data and attach insights to specific customer profiles. With it, companies are discovering sentiment, preference, and predictive insights that guide smarter segmentation, better targeting, and more intelligent treatment strategies. In short, when you can master your data at scale, there s no limit to the external data sources that can feed your analytics. State Street Bank s trusted repository Our MDM technology helps State Street Bank maintain a reliable hub that manages counterparty data from internal and external sources, linking it to counterparty contracts, positions, and credit ratings. It gives the bank a single point of reference to make accurate risk calculations with ultimately lower regulatory capital requirements. Informatica How Master Data Management powers big data decision making. 10

11 Three Enrich your data back into systems of engagement. Big data MDM lets you take businesscritical data from many systems, decouple it from the underlying applications, and then master it in one place. But even though it s a central repository of the best version of all that data, it actually goes one step further and feeds all that good clean data back into the underlying applications fueling customer engagement, analytics and operations. In this way, you keep your data where it lives while ensuring the cleansed, integrated view of key dimensions is passed back to the systems your people use every day. So every CRM has the same, mastered record of Mr. George Kessler and each system is enhanced by the learning from the other systems via the central MDM. The ability to maintain a centralized repository of trustworthy, cleansed, and mastered records is central to ensuring your big data projects run efficiently. From better insights to lower costs, the benefits are hard to ignore. Informatica How Master Data Management powers big data decision making. 11

12 Don t try big data without MDM. At a big data scale, the stakes are higher, the data is harder to manage, and the potential for insight is greatest. But without a reliable, repeatable method for matching and mastering all your records, the success of your big data projects hangs in the balance. Without MDM, you waste scarce, expensive data scientists on frustrating, manual data preparation work. Even worse, error-prone data preparation leads to unreliable or inaccurate insights. Finally, the time and money that goes into basic data management inhibits experimentation, foregoing important opportunities. When you leverage Master Data Management, you bring order and discipline to the otherwise unwieldy challenge of managing big data chaos: Focus your expensive talent on analysis and data science. Empower your data stewards and analysts to deliver better insights, more efficiently. Give your data architecture a built-in, repeatable approach to cleansing, integrating, and storing reliable, authoritative data. When big data projects fail, it s typically 1 because they cost too much, take too long, or fail to deliver on bold promises. So it stands to reason the key to big data success lies in creating more efficient, sustainable, and reliable processes for analyzing large amounts of data. In short, it s impossible to ignore the benefits of Master Data Management in a big data world. 1 Eight reasons big data projects fail, InformationWeek Informatica How Master Data Management powers big data decision making. 12

13 Further reading. The Big Big Data Workbook MDM plays a big role in big data decision-making. You might like our workbook on this subject, The Big Big Data Workbook. Read it now. The Big Big Data Workbook A practical guide to get your first big data project off the ground. Informatica How Master Data Management powers big data decision making. 13

14 IN About Informatica. We re Informatica, and we re helping enterprises of all sizes tackle big data. Our Hadoop-native MDM solution helps our customers derive 360 views of their customers, products, suppliers, and locations and empowers their business users with a suite of MDM-fueled applications. Our data management solutions make large enterprises decision ready. Let s talk. Informatica How Master Data Management powers big data decision making. 14