Stop #6 in the Big Data Archipelago journey: the Data Governance Isle

 “Data that is loved tends to survive.” – Kurt Bollacker, data scientist

Awhile back, I had the pleasure of traveling to five countries in Europe to participate in a 10-day, SAS-sponsored executive roadshow. Jill Dyché, Vice President of SAS Best Practices, kicked off each event with a discussion on data governance, data management and treating data as a corporate asset. I then spoke about big data and going beyond the 3Vs, similar in concept to this blog series. After each event, we then had the opportunity to talk with SAS customers about their data governance and big data issues.

It is clear that data governance and data management continue to be important discussions – especially with big data on the scene. This was evident in Europe. While many companies have already developed (or are developing) an enterprise-scale data governance program, there’s some who still don’t understand its significance and/or are simply afraid to have the discussion. For those who do get it, though, they’re beginning to ask the question, “What do data governance and management look like for big data?”

The Data Governance Isle in the Big Data Archipelago

The Data Governance Isle in the Big Data Archipelago

A Big Data Best Practice for Data Governance

One casual observation I made on my trip is that Europeans, unlike their US counterparts, seem more cautious about jumping into one-off, sandbox-type, big data projects. They seem more interested in having the data governance and management discussion up front, or at least while they’re going along. In the US, a more common approach is to wrestle with big data first, then figure out how to govern and manage it later on – if the project proves successful. Perhaps there’s a happy medium.

Given that we’re still in the emerging phase of big data, the approach we’re recommending is to tackle data governance one big data project at a time. Determine which components of data governance – i.e., decision-making bodies, workflow, decision rights, data quality, and rules of engagement, to name a few – make sense for each big data project. Not all governance components will apply and that’s okay.

After you have a few big data projects under your belt, you should have a solid data governance framework that supports all data, big and small. In other words, you won’t need to develop a framework specifically for big data governance.

A Best Practice Checklist for Data Governance

Where does your company stand when it comes to data governance? Do you have a framework in place? If you’re just getting started, here’s a few quick practices we recommend:

  • Define what data governance means—to your company and to your project.
  • Know your culture. One size does not fit all. Some organizations are better suited for a top-down approach, while others will work better from the bottom up.
  • Design your data governance framework. Identify the “what” and “how” before specifying the “who.” Leverage existing committees and processes.
  • Treat data governance as a long-term program. Implement it as a series of tightly-scoped initiatives. Plan for the activities and resources required to execute and maintain governance policies.

Organizations that already embrace centralized or shared services that integrate with functional business processes will have a less difficult path than those starting from scratch. However, the effort to establish meaningful and sustainable data governance and management will still:

  • Require a business context considered relevant and valuable to the end users;
  • Make mistakes that may require multiple attempts before results are sustained; and
  • Depend on a determined commitment to achieve the vision of data as a corporate asset and a willingness to learn from mistakes and try again.

As I mentioned earlier, when it comes to big data, you don’t need to develop a separate data governance program or framework. You just need a data governance program and framework that supports big data. Yes, I know. Easier said than done.

Key Takeaways for Marketers

  • If you’re ever in Europe, go medieval and visit a SAS castle.
  • Coordinate your island schedule with other members on the data governance team. Solo trips are not allowed.
  • Don’t skip corners with “big” data. It’s all data. It needs to be governed along with the rest of your data.
  • Visit the chalets on the South Shore of the island. They have hot tubs, practically bubbling over with new insights.
  • Don’t be afraid to fail. Big data is new. We’re all learning, and the opportunities usually outweigh the challenges.

This is the 6th post in a 10-post series, “A marketer’s journey through the Big Data Archipelago.” This series explores 10 key best practices for big data and why marketers should care. Our next stop is the Data Security Isle, where we’ll talk about treating data as a corporate asset.

 

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We really must opt-out of annoying our customers

In the last two weeks I've put some effort into opting-out of emails. I can't read them all because simply get too many, so something has to give. In some cases, I signed up for the newsletter or opted in to something with every intention of keeping up, but it just became too much. In other cases, my name has been added to some list and then it got sold, so I get all sorts of emails from people and organizations I’ve never heard of. So, if I haven’t opened your email in the last few months, then I've likely unsubscribed from your list - don't take it personally.

Unsubscribing can be simple and straightforward – one click and done. Other times, you might first be asked "why you are leaving." That’s a little more work, but it’s also nice because you get the impression that they care what you think. In any case - choose the drop-down or enter the reason in the text field, and it’s done. Simple.

Not allowing a full opt-out is annoying.

Really? How about not getting ANY more emails from you?

And then there are the ones that keep coming and coming. They offer the perfunctory unsubscribe process, but sometimes you are only able to opt-out of getting emails from specific people in their organization, or from receiving emails on a specific topic. It doesn't seem to matter because you still get the emails but from different people or about different topics and it's the same organization doing the sending.

Not allowing full email opt-outs is annoying.

You mean I have to individually opt-out of all 22 of your categories for your emails to stop??

The emails are really from the same company putting their same spray-and-pray hard sell in an email with another oh-so-clever subject line. To me - that’s annoying.

It’s disrespectful and it makes me want to not do business with you.

There’s one national retailer that I’ll leave unnamed that lets me upload digital pictures and pick them up at the store by my house. Read More »

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Move from mass marketing to customer decision management

You may notice that marketing is changing. Fewer credit card offers are hitting your mailbox. Fewer coupons for irrelevant products and services. There is a reason for the change. Organizations are beginning to understand that mass marketing alienates customers. Spray and pray doesn't work - so organizations aren't spending their money on it any longer - at least not to the degree that we used to see. In the next few years, as mass marketing continues to diminish, loyalty from consumer to brands should increase, and the trust that marketers earn from consumers will become more noticeable.

So that's then, but this is now. What are organizations doing now to focus on customer decision management? What are brands doing to make sure that the offers they deliver are personalized and relevant?

In May 2014, SAS commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examined the potential return on investment (ROI) enterprises may realize by deploying SAS Real-Time Decision Manager - our solution for customer decision management.

The purpose of this study was to provide readers with a framework to evaluate the potential financial impact of Real-Time Decision Manager (RTDM) on their organizations. To better understand the benefits, costs, and risks, Forrester interviewed an existing customer — a retail bank — with more than a year of experience using RTDM. Read More »

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Stop #5 in the Big Data Archipelago journey: the Location Isle

Never underestimate the bandwidth of a station wagon full of tapes hurtling down the highway.” – Andrew S. Tanenbaum

There’s been a lot of buzz about the Location Isle for several years due to a strong interest in its cloud computing territories – private cloud, public cloud and hybrid cloud. Whereby this island is all about where to store the data, it works closely with the Data Security Isle (our next stop) to ensure that the data is protected, regardless of where it resides.

Despite the Location Isle’s popularity, and akin to the Processing Isle, you won’t find many marketers spending a lot of time here (but perhaps they should). They’re more interested in having secure and quick access to the data they need – big and small – and less interested in understanding the where’s and why’s of data storage. But a quick stop here helps marketers clear the air for what would otherwise be a nebulous discussion about big data with their I.T. counterparts.

Notwithstanding, we have traditionally centralized our data and platforms on-premises in our own data center(s). This has given us the most control over and provided the best security for our data. In the last few years, however, cloud computing has disrupted this strategy, providing additional storage options for both IT and the business at a fraction of the cost. And not that this conversation wasn’t interesting enough (relatively speaking), we now have big data.

The Location Isle in the Big Data Archipelago

The Location Isle in the Big Data Archipelago

A Big Data Best Practice for Location

The beauty with big data is that it provides a compelling use case for the cloud, while the cloud provides the resources needed to support big data. Some would even say they’re a match made in heaven. Read More »

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Stop #4 in the Big Data Archipelago journey: the Open Source Adoption Isle

“One does not discover new lands without consenting to lose sight of the shore for a very long time.” - André Gide

Ever heard of OpenOffice, Hadoop, Android, Firefox or MySQL? If so, can you identify the common denominator between these software tools and applications? If you answered, “They’re all open source,” you’re right!

While open source software has been around a long time, many organizations have been somewhat slow on the draw to integrate open source into their enterprise infrastructure. A lot of companies have considered open source solutions for initiatives such as BI/DW and have compared them with proprietary solutions on functionality and cost. Yet the hard truth is: We’ve known about these open source solutions, and still we’ve been able to get by without them on a large scale.

Until now.

The Open Source Adoption Isle in the Big Data Archipelago

The Open Source Adoption Isle in the Big Data Archipelago

A Big Data Best Practice for Open Source Adoption

With the rapid growth of big data solutions these last few years, open source has taken a significant step forward into the enterprise space. Conversely, more and more enterprise-level organizations have begun to participate in and contribute code to the open source community. The time has come to take open source seriously for big data platforms. Read More »

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Facebook vs. Orkut – three lessons for marketers

Me:          Hey! Orkut is going away.
You:         Oh, bummer! I didn't realize you had one of your cousins from Hungary in town.
Me:          *sigh*

A Facebook exchange among Social Media experts about Orkut.

I got permission to use this screen-shot of the exchange.

One of my Facebook friends recently mused in a post about getting a farewell email from Orkut. And she couldn’t remember what Orkut was. To me – that tells the whole story of Orkut. And in her case, she is a well-recognized authority on social media - and not remembering Orkut doesn’t undermine her expertise in the least. The problem is clearly about Orkut. And then I got to thinking:

How in the world
could a company like Google
develop something like Orkut
and have it be a flop?

Long story short – Orkut is a social messaging platform that Google developed and launched in January, 2004. It is substantially similar to Facebook, which launched in February, 2004. For more details about Orkut, visit the Wikipedia page. As for Facebook, I doubt you need a Wikipedia page to know about it.

I am not doing a tit-for-tat comparison of features and functionality about Orkut vs. Facebook because it doesn’t matter – Facebook won the battle. But I do think there are at least three marketing strategy lessons to be learned here.

Names matter

No, not your name – the name of your product / company. It helps if the name vaguely describes what it does, and it’s even better if it describes how it benefits the user. It’s not always possible to assign a name that way, but if so – do it. And find something easy to pronounce in multiple languages. Read More »

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Stop #3 in the Big Data Archipelago journey: the Integration Isle

“If you build it, he will come.” – From the movie “Field of Dreams”

“Build it and they will come” is a popular quote often attributed to the movie Field of Dreams. But guess what? This quote is not from the movie; it’s actually a misquote. [See the actual quote above.] It’s fascinating how much mileage this misquote has gained over the years—in the media, at conferences, in our business meetings, and even in our social circles.

Truth be told, this quote—right or wrong—has fueled our organizations: Build the data warehouse and they will come. Build the customer data mart and they will come. Build the analytics solution, the self-service BI app, the data visualizations – and they will come. Even though we go to great lengths to expand our platforms, build the applications, and integrate the data and business processes, the reality is that they don’t always come. Cindi Howson, founder of BI Scorecard, has done the research and tells us the same story every year: BI Adoption Flat.

But now that we have big data and can build it into the mix, will they finally come?

Figure 1. The Integration Isle in the Big Data Archipelago

Figure 1. The Integration Isle in the Big Data Archipelago

A Big Data Best Practice for Integration

The best practice we like to share here is: Build it on demand using the best tools for the job. With big data and its technologies, we now have more options on what, where, and how we’re going to build our integrated infrastructure. Read More »

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Set your compass to positivity for best results

Do your winds point to the "P" for "Positivity" on the weather vane?

Do your winds point to the "P" for "Positivity" on the weather vane?

I like to be reminded from time to time that having a positive attitude has so many better outcomes than the opposite approach. Some people try to make the case that focusing on positive outcomes actually drives positive outcomes, which sounds idealistic, but I think there's some truth to that idea.

One such positively-inclined person made a big impact on me seven years ago on my first day on the job at SAS - Newt Gingrich.

No, seriously - he spoke as Keynote at the SAS Health Analytics Executive Conference, which I attended that day. And while he's not best remembered for all-positive actions, he has established himself as an authority on the health care ecosystem as Co-founder of the Center for Healthcare Transformation.

Mr. Gingrich believes that in a contentious situation (like debating health care reform), if all sides simply stopped looking for reasons not to do something and instead focus on what needs to be addressed to make it possible, then the conversation will move toward a positive resolution. Simply put, he believes we should:

Stop saying "No, because..." and instead say, "Yes, if..."

That prescription may / may not be how health care reform actually happened, but he does make a valid point. Just think about it.

More recently, I've been inspired to share thoughts about the power of a positive attitude from an internal blog post written by Fritz Lehman, SVP of Customer Engagement and Support at SAS. His post is titled, "Relentlessly Positive," and I like what he has to say:

---------- Read More »

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Stop #2 in the Big Data Archipelago journey: the Processing Isle

“I have travelled the length and breadth of this country and  talked with the best people, and
I can assure you that data processing is a fad that won’t last out the year.”
(Editor in charge of business books for Prentice Hall, 1957)

Whereby the Analytics Isle tends to be a popular destination for marketers on the big data journey, you really won’t find them flocking to the nearby Processing Isle. This highly active island has much to offer—like special territories for batch, real-time, and streaming data—but marketers aren’t typically interested in how data is processed, as much as they’re interested in what marketing data can be processed and how fast. The happy folks on the Processing Isle keep them happy with timely, reliable, and relevant data. How the data gets there, many don’t care or need to care.

Figure 1 - The Processing Isle in the Big Data Archipelago

Figure 1 - The Processing Isle in the Big Data Archipelago

Regardless, marketers who have been in the industry awhile have witnessed the remarkable speed at which data warehousing technologies have advanced over the years. Nowadays, not only do we have options on how to process our data—such as grid computing, in-database, in-memory, and appliances—we also have much greater control over the activity in our data warehouse and analytical ecosystems. With these advancements, we’ve been able to increasingly optimize the data warehouse around mixed workloads, and marketers are undeniably reaping the benefits.

A Big Data Best Practice for Processing Data

Even with the significant technological advancements in traditional systems, big data technologies have changed the playing field for processing data of all shapes and sizes. Read More »

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Stop #1 in the Big Data Archipelago journey: the Analytics Isle

“There are known knowns. These are things we know that we know.
There are known unknowns.  That is to say, there are things that we know we don't know.
But there are also unknown unknowns. There are things we don't know we don't know.”
- Donald Rumsfeld

Welcome to the Analytics Isle—the #1 hot spot destination for marketers in the Big Data Archipelago! It’s not hard to understand why this island is so popular given its unlimited data opportunities for exploration, reporting, advanced analytics and data visualization. With all the data that’s available these days—from traditional data (CRM, contact center, sales) to big data (email, social, mobile)—some marketers are having a heyday carving out new paths and adventures to pursue, while others are simply stuck in the moat.

Big Data Archipelago - Analytics Isle

Figure 1. The Analytics Isle in the Big Data Archipelago

For years, marketers have used analytics with their traditional data to gain valuable insight into their company and customers. In other words, with analytics, they have come to the data with their business questions in hand to answer, what we will call, the known unknowns. They know what they don’t know or want to know, and they use analytical data to fill in the blanks. It’s like discovering gold in a hidden treasure chest of data.

Discovering the known unknowns with analytics has kept companies very busy for years and will continue to do so in the years to come. Now let’s shift our focus from what is happening with traditional data and analytics and explore what could be happening with big data. Read More »

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