Tag Archives: Analytics
I’m glad to hear you feel comfortable explaining data to your friends, and I completely understand why you’ll avoid discussing metadata with them. You’re in great company – most business leaders also avoid discussing metadata at all costs! You mentioned during our last call that you keep reading articles in the New York Times about this thing called “Big Data” so as promised I’ll try to explain it as best I can. (more…)
Customers don’t always like change, and new product launch offers variety of changes so it’s important to showcase the value of the change for customers while launching a product. One key ingredient that can fuel the successful Product launch is leveraging the rich, varied, multi-sourced, readily available information. Yes, tons of information which is like a gold mine and is available to us more easily/readily than ever before from various different sources. Industry experts call it Big Data. Today Big Data can pull gold out of this information gold mine and positively impact a product launch. What follows are 3 secrets of how Product Marketers can tap the power of Big Data for a successful product launch.
Secret #1: Use Big Data to optimize content strategy and targeted messaging
The main challenge is not just to create a great product but also to communicate the clear compelling value of the product to your customers. You need to speak the language that resonates with needs and preferences of customers. Through social media platforms and weblogs, lots of information is available highlighting views/preferences of buyers. Big Data brings all these data points together from various sources, unlocks them to provide customer intelligence. Product Marketers can leverage this intelligence to create customer segmentation and targeted messaging.
Secret #2: Use Big Data to identify influential customers and incent them to influence others
One of the studies done by Forrester Research indicates that today your most valuable customer is the one who may buy little but influences 100 others to buy via blogs, tweets, Facebook and online product reviews. Using MDM with Big Data businesses can create a 360 degree customer profile by integrating transaction, social interaction and weblogs which help in identifying influential customers. Companies can engage these influential customers early by initiating a soft launch or beta testing of their product.
Secret #3: Use Big data to provide direction to ongoing Product improvement
Big Data is also a useful tool to monitor on-going product performance and keeping customers engaged post-launch. Insights into how customers are using the product and what they enjoy most can open the doors for improvements in future launches resulting in happier and loyal customers.
Zynga, creator of most popular Facebook game Farmville, collects terabytes of big data in a day and analyzes it to improve the game features and customer services. As indicated in a WSJ article after Version 1 launch of the game, the company analyzed customer behavior and found that customers were interacting with animals much more than the designers expected. So in the second release game designers increased the game offerings with more focus on animals keeping customer’s more engaged.
Big data is proving to be a game changer for product managers and marketers who want to deeply engage with their customers and launch products with a memorable and valued customer experience.
Science fiction represents some of the most impactful stories I’ve read throughout my life. By impactful, I mean the ideas have stuck with me 30 years since I last read them. I recently recalled two of these stories and realized they represent two very different paths for Big Data. One path, quite literally, was towards enlightenment. Let’s just say the other path went in a different direction. The amazing thing is that both of these stories were written between 50-60 years ago. (more…)
In my recent blog posts, we have looked at ways that master data management can become an integral component to the enterprise architecture, and I would be remiss if I did not look at how MDM dovetails with an emerging data management imperative: big data and big data analytics. Fortunately, the value of identity resolution and MDM has the potential for both contributing to performance improvement while enabling efficient entity extraction and recognition. (more…)
Evolving from Chaos to Competitiveness: The Emerging Architecture of Next-Generation Data Integration
To compete on Big Data and analytics, today’s always-on enterprise needs a well-designed evolving high-level architecture that continuously provides trusted data originating from a vast and fast-changing range of sources, often with different formats, and within different contexts.
To meet this challenge, the art and science of data integration is evolving, from duplicative, project-based silos that have consumed organizations’ time and resources to an architectural approach, in which data integration is based on sustainable and repeatable data integration practices – delivering data integration automatically anytime the business requires it. (more…)
In my previous blog I explored the importance of a firm understanding of commercial packaged applications on data quality success. In this final post, I will examine the benefits of having operational experience as a key enabler of effective data quality delivery. (more…)
Integration technologies have been around for 20 years (as long as Informatica has been in business) and have proliferated in corporate IT. We are now at an inflection point in the business needs and maturity of integration best practices which we can call Next Generation Data Integration (DI). If we’re going to talk about the next generation, then first we need to put a stake in the ground to describe the current, or prior generation. Furthermore, for it to be a “generational” change, it needs to be a significant step-function improvement in how the work is done and in the business value generated by data assets. Or as Jim Collins said in Built to Last: Successful Habits of Visionary Companies, we need a Big Hairy Audacious Goal. (more…)
Finally, there is now evidence of a clear link between financial performance and the broad use of data by employees. Specifically, organizations that take the lead in data analytics are more than three times more likely to be leaders within their industry groups than companies with standard analytics environments.
That’s the finding of a new survey of 530 senior executives, conducted by the Economist Intelligence Unit. There is little disagreement that the ability to make data available across the entire enterprise means greater productivity and performance. More than 80 percent of respondents believe that employees across their organizations “can and should be using data to do their jobs.” (more…)
The reality in data warehousing is that the primary focus is on delivery. The data warehouse team is tasked with extracting, transforming, integrating, and loading data into the warehouse within increasingly tight timeframes. Twenty years ago, monthly data warehouse loads were common. Ten years ago, weekly loads became the norm. Five years ago, daily loads were called for. Nowadays, near-real-time analytics demands the data warehouse be loaded more frequently than once a day. (more…)