Getting Value from Your Data Series: It’s All About Your Data Ecosystem

By Marilyn Craig and Mary Ludloff

We’re back with the fourth post in our series on how to get value from your data, including how to ensure that new “data” and “analytics” products are designed for successful delivery to new and existing customers.

In the previous posts in this series, we discussed our methodology and what is required in terms of understanding your target customer—who they are and what they need—as well as making sure you have the right Team in place to work on the project. In this post, we are going to discuss how you build your Data Ecosystem:

• What is needed to ensure that data processes will support the new product(s)?
• How do you identify appropriate data partners and enhancements?
• What privacy- and security-related issues must you be aware of and address?

Getting Value From Your Data Series: The Road May Be Rocky But It’s Well Worth the Effort!

By Mary Ludloff and Marilyn Craig

Unless you’ve been asleep for the past couple of years, you, like us, have heard this phrase again and again:  Data is the new oil.  It certainly sounds great but what exactly does it mean? Here’s our take: Getting the most value out of your data can make you better at what you do as well as enable you to do more with what you have. In other words, there’s unrealized value in those data silos that all companies have. But make no mistake: the road to realizing data value is paved with good intentions and often times, poor execution and results.

Today, most companies are drowning in data—there’s historical data from operations, data from public sources, data from partners and acquisitions, data you can purchase from data brokers, etc.  These companies have read all the research and want to leverage their data assets to make “better” operational decisions, to offer their existing customer base more insights, to pursue new revenue opportunities. Of course, the real value in that data is derived from the business analytics that deliver the insights that drive better decisions. As we’ve said quite often on this blog: Data, without the proper use of analytics, is meaningless. If data is the new oil, think of analytics as the oil drills—you need both to be successful.

Big Data Project: Objectives First, Plan Second (Part 3)

A top-level view of our data project over a series of posts.
By Mary Ludloff

Welcome to the third post in our series on a big data project. Our goal is to walk you all the way through a big data project from its inception through its completion (or depending on the project, through deployment and maintenance). Those of you familiar with our series know that we include our Big Data Playbook rules as we address specific topics—we may repeat some as we go along but if you need to refresh your memory on where we are, go to Part 1 and Part 2.

You now know that we are working with the University of Sydney on a project that looks at the impact social media comments have on a company’s stock and whether this mediates the influence of primary news. Specifically: Is a company’s stock price influenced by both and can we isolate and study the impact of those distinct sources on that stock price?

Big Data Project: Start with a Question that You Want to Answer

A top-level view of our data project over a series of posts.

By Marilyn Craig

Welcome to the second post of a series on a big data project that will (Mary and I hope) provide clarity and insights on how to successfully complete a big data initiative. Now, just in case you’ve forgotten the first two rules in our Big Data Playbook, I am going to repeat them here because they play into our topic of the day which is all about “starting” your big data project:

Rule #1: Big Data IS NOT rocket science.

Yes, far too often those lucky internal folks tasked with managing a big data project fall into the trap of data science paralysis which is similar in thought to analysis paralysis. By this I mean that there are so many moving pieces to capture, so many technology decisions to make, so many skill positions that need to be filled, so many fill-in-the-blanks that need to get done that you never actually get started which leads me to our second rule:

Rule #2: Garbage in, garbage out.

Big Data Project: Let’s Start at the Very Beginning—The Big Data Playbook

By Mary Ludloff

In my last post, I wrote about the three V’s of big data and why there are only three. There has been a messaging pile-on that seems to be happening in the big data space that even I, long-time marketer, find disconcerting. So, over the course of a number of posts, my colleague, Marilyn Craig, and I are going to de-mystify a big data project, taking apart each stage of a real big data initiative as if it were a release post-mortem. We will be talking about roles and responsibilities, data governance, project and process management, what went right, what went wrong, what we should have done differently. Except in this case, it will not be after the fact but rather a stage-by-stage review as we work on a real-world project. For your sanity and ours, we have created a special category, Big Data Project, as well as a tag with the same name. If you search on either, you will see all posts related to the project. Additionally, all posts about the project will start with Big Data Project in the title. Who knows? Maybe when we’re done, we’ll write a book (knowing what I know now about writing a book, I can’t believe I just said that)!

We’ll talk more about the project in the next post but first I wanted to take a look at a big data failure that anyone involved in a major enterprise application deployment could have seen coming and is Rule #1 in our big data playbook:

Rule #1: Big Data IS NOT rocket science.