sas global forum executive conference

9月 122015
 

If a data lake isn’t a data warehouse, as I proposed in my last post, then it behooves us to better understand more about this “new” data lake structure. In the fifth and final post in this series titled, Big Data Cheat Sheet on Hadoop, we’ll highlight some of the pros and cons of a data lake using a SWOT diagram.

Question 5: What are some of the pros and cons of a data lake?

This discussion comes from an online debate I had earlier this year with my colleague, Anne Buff, where we discussed the pros and cons of a data lake in context of this resolution: The data lake is essential for any organization that wants to take full advantage of its data. I took the Pro stance, while Anne took the Con stance.

Even though our online debate was focused on the data lake, it forced us to address the larger discussion of managing growing volumes of data in a big data world. With the onslaught of big data technologies in recent years—the most popular being the open source project, Apache Hadoop—organizations are having to look once again at the underlying technologies supporting their data collection, processing, storage, and analysis activities.

The Hadoop-based data lake happens to be a popular option right now. The SWOT diagram below identifies some of the key factors when considering a data lake. Keep in mind that this is just a quick snapshot (with brief explanations following), and not a comprehensive list:

datalake-swot

Strengths

  • Lower costs. A Hadoop-based data lake is largely dependent on open source software and is designed to run on low-cost commodity hardware. So from a software and hardware standpoint, there’s a huge cost savings that cannot be ignored.
  • One-stop data shopping. Hadoop is no respecter of data. It will store and process it all – structured, semi-structured, and unstructured—at a fraction of the cost and time of your existing, traditional systems. There’s much to be gained from having all (or much of) your data in one place – mixing and matching data sets like never before.

Weaknesses

  • Data management. We can get hung up talking about the volume, variety, and velocity of (big) data, but equally important to this discussion is being able to govern and manage all of it, regardless of the underlying technologies. For a Hadoop-based data lake, both open source projects and vendor products continue to mature/be developed to support this increasing demand. We’re moving in the right direction—rapidly—but we’re not quite there yet.
  • Security. Hadoop-based security has been a long-time issue, but there’s significant effort and progress being made by the open source community and vendors to support an organization’s security and privacy requirements. While it’s easy to finger wag at this particular “weakness,” it’s important to recognize that the weekly (and almost daily) reports we hear about this-&-that data breach are primarily attacks on existing traditional systems, not these newer big data systems.

Opportunities

  • Discovery. This feature allows users to discover the “unknown unknowns.” Unlike existing data warehouses where users are limited with both the questions and answers they can ask and get answers for, with a Hadoop-based data lake, the sky’s the limit. A user can go to the data lake with the same set of questions she had for the data warehouse and get the same, or even better, answers. But she can also discover previously-unknown questions, thus driving her to more answers, and ideally, better insights.
  • Advanced analytics. A lot of software apps include descriptive analytics, showing a user pretty visuals about what’s happened. We’ve had this capability for decades. With big data, however, organizations need advanced analytics—such as prescriptive, predictive, and diagnostic—to really get ahead of the game (and one could even argue to stay in the game). A Hadoop-based data lake provides that opportunity.

Threats

  • Status quo. This is not a new threat, especially for software vendors, but it’s a very real threat. The cost and time required to migrate towards these newer big data technologies is not insignificant. This is not a case of hot-swapping technologies while no one is looking. It will also impact the people, processes, and the culture in your organization—if done right.
  • Skills. There is no question that there is a skills shortage for these big data technologies. Even though this shortage can be viewed as a threat to Hadoop adoption, it shouldn’t be seen as a negative. These big data technologies are new, they’re evolving, and there’s a lot of experimentation going on to figure out what’s needed, what’s not, what should stick, what shouldn’t, etc. Thus, it should be no surprise that as our technologies evolve, so will the skills required. We have an opportunity to take what we have and know to a new level and help prepare the next generation to excel in our data-saturated society.

The bottom line. There are well-known weaknesses and threats associated with a data lake—some of which I have highlighted here—and we cannot ignore these. But there are also significant strengths and opportunities to explore. I believe an organization can take full advantage of its data if there’s a way for them to bring it all together without breaking the bank. A data lake can help make this dream a reality.

This is the final post in a 5-part series, "Big Data Cheat Sheet on Hadoop." This spin-off series for marketers was inspired by a popular big data presentation I delivered to executives and senior management at a recent SAS Global Forum Executive Conference.


Editor’s note:

If you did not read the previous posts in this series, I encourage you to read those as well. Tamara's goal here has been to enable you to have an informed view of how this area of technology can support your marketing strategy. Armed with these perspectives, hopefully you can partner even more closely with I.T. and operations to deliver the best possible customer experience.

Once you're comfortable with Hadoop and want to delve deeper into analytically-driven marketing solutions, start with our Customer Intelligence home page at: www.sas.com/customerjourney.

And as always, thank you for following!

 

tags: big data, Big Data Cheat Sheet on Hadoop, customer experience, Hadoop, sas global forum executive conference

Marketers ask: What are some of the pros and cons of a data lake? was published on Customer Analytics.

8月 282015
 

In this 5-part blog series on the Big Data Cheat Sheet on Hadoop, we’re taking a look at these five questions from the perspective of a marketer:

Image showing a serene lake at sunset.

This lake is NOT a data lake.

  • What can Hadoop do that my data warehouse can’t?
  • Why do we need Hadoop if we’re not doing big data?
  • Is Hadoop enterprise-ready?
  • Isn’t a data lake just the data warehouse revisited?
  • What are some of the pros and cons of a data lake?

We’ve already tackled the first three questions, and we’re now on question 4, so it’s time to talk about the data lake.

Question 4: Isn’t a data lake just the data warehouse revisited?

Some of us have been hearing more about the data lake, especially during the last six months. There are those that tell us the data lake is just a reincarnation of the data warehouse—in the spirit of “been there, done that.” Others have focused on how much better this “shiny, new” data lake is, while others are standing on the shoreline screaming, “Don’t go in! It’s not a lake—it’s a swamp!”

All kidding aside, the commonality I see between the two is that they are both data storage repositories. That’s it. But I’m getting ahead of myself. Let’s first define data lake to make sure we’re all on the same page. James Dixon, the founder and CTO of Pentaho, has been credited with coming up with the term. This is how he describes a data lake:

“If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”

And earlier this year, my colleague, Anne Buff, and I participated in an online debate about the data lake. My rally cry was #GOdatalakeGO, while Anne insisted on #NOdatalakeNO. Here’s the definition we used during our debate:

“A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. The data structure and requirements are not defined until the data is needed.”

The table below helps flesh out this definition. It also highlights a few of the key differences between a data warehouse and a data lake. This is, by no means, an exhaustive list, but it does get us past this “been there, done that” mentality:

dw-vs-datalake

Let’s briefly take a look at each one:

  • Data. A data warehouse only stores data that has been modeled/structured, while a data lake is no respecter of data. It stores it all—structured, semi-structured, and unstructured. [See my big data is not new The data warehouse can only store the orange data, while the data lake can store all the orange and blue data.]
  • Processing. Before we can load data into a data warehouse, we first need to give it some shape and structure—i.e., we need to model it. That’s called schema-on-write. With a data lake, you just load in the raw data, as-is, and then when you’re ready to use the data, that’s when you give it shape and structure. That’s called schema-on-read. Two very different approaches.
  • Storage. By definition, a relational data warehouse stores data in a hierarchical manner, while a data lake is object-based and is not dependent on any hierarchy.
  • Agility. A data warehouse is a highly-structured repository, by definition. It’s not technically hard to change the structure, but it can be very time-consuming given all the business processes that are tied to it. A data lake, on the other hand, lacks the structure of a data warehouse—which gives developers and data scientists the ability to easily configure and reconfigure their models, queries, and apps on-the-fly.
  • Security. Data warehouse technologies have been around for decades, while big data technologies (the underpinnings of a data lake) are relatively new. Thus, the ability to secure data in a data warehouse is much more mature than securing data in a data lake. It should be noted, however, that there’s a significant effort being placed on security right now in the big data industry. It’s not a question of if, but when.
  • Users. For a long time, the rally cry has been BI and analytics for everyone! We’ve built the data warehouse and invited “everyone” to come, but have they come? On average, 20-25% of them have. Is it the same cry for the data lake? Will we build the data lake and invite everyone to come? Not if you’re smart. Trust me, a data lake, at this point in its maturity, is best suited for the data scientists.

Why this matters

At the most fundamental level, big data is mostly driven by customer-related activity, and Hadoop is a very effective way to handle big data. And as a marketer, you may hear rumblings that your organization is setting up a data lake and/or your marketing data warehouse is a candidate to be migrated to this data lake. It’s important to recognize that while both the data warehouse and data lake are storage repositories, the data lake is not Data Warehouse 2.0 nor is it a replacement for the data warehouse.

So to answer the question—Isn’t a data lake just the data warehouse revisited?—my take is no. A data lake is not a data warehouse. They are both optimized for different purposes, and the goal is to use each one for what they were designed to do. Or in other words, use the best tool for the job.

This is not a new lesson. We’ve learned this one before. Now let’s do it.

This is the 4th post in a 5-part series, "Big Data Cheat Sheet on Hadoop." This spin-off series for marketers was inspired by a popular big data presentation I delivered to executives and senior management at a recent SAS Global Forum Executive Conference.


Editor’s note:

If you did not read the previous posts in this series, I encourage you to read those as well. Tamara's goal with this series is to enable you to have an informed view of how this area of technology can support your strategy. Armed with these perspectives, hopefully you can partner even more closely with I.T. and operations to deliver the best possible customer experience.

Once you're comfortable with Hadoop and want to delve deeper into analytically-driven marketing solutions, start with our Customer Intelligence home page at: www.sas.com/customerjourney.

And as always, thank you for following!

tags: big data, Big Data Cheat Sheet on Hadoop, data lake, Hadoop, sas global forum executive conference

Marketers ask: Isn’t a data lake just the data warehouse revisited? was published on Customer Analytics.

8月 142015
 

In response to my last post—Marketers ask: Why do we need Hadoop if we’re not doing big data?—a Tweet: "Why should marketers worry about Hadoop at all?"Twitter follower asked this question:

It’s a fair question. Typically, marketers are more interested in the car (in this case, big data) than they are in the engine (Hadoop). But Hadoop is not just another faster, more cost-effective engine option. It’s a game changer in the world of data management—much like the Prius and Tesla have been in the world of gas-guzzling cars, trucks, and SUVs.

Do marketers need to understand how Hadoop works? Not at all. But what should interest them is if and how this popular big data technology can help them gain better and more informed insights about their customers. If (big) data can indeed help take the customer experience from a 3-star to a 5-star experience, then isn’t it worth understanding what all the Hadoopla is about?

This dovetails nicely into our 3rd question in this 5-part series. My answer will be short—and it may surprise you.

Question 3: Is Hadoop enterprise-ready?

I have two answers to this question:

  • For your organization: Maybe.
  • For all organizations: No.

It all depends on what and why you want to use Hadoop in your organization. If you simply want to use it as an additional (or alternative) storage repository and/or as a short-term data processor, then by all means, Apache Hadoop is ready for you. (My last post discusses six ways Apache Hadoop can be used.)

However, if you want to go beyond data storage and processing, and you’re looking for some of the same data management and analysis capabilities you currently have with your existing data ecosystem, Apache Hadoop alone is not going to cut it.

As I mentioned in my first post, you will need to get technical assistance—from IT and developers, internal and external—to explore the vast ecosystem of Hadoop-related open source and proprietary projects and products to achieve your objectives. This will not be a small undertaking. Remember, you don’t want to “do Hadoop” just because everyone else is doing it or because it looks good on paper or it’s cheap for IT to install. You want to do Hadoop if it helps address or solve real business issues your organization is facing. Start with your requirements list first before you start looking at Hadoop.

One final point to consider is that many of these newer Hadoop-related technologies are still maturing—quite rapidly, I might add. They don’t have the decades of R&D behind them like our existing relational systems. That’s not a strike against Hadoop; it’s just the reality of where we are today. That’s why I say Hadoop—as in the Hadoop ecosystem—isn’t 100% ready for the enterprise. Yet.

This is the 3rd post in a 5-part series, "Big Data Cheat Sheet on Hadoop." This spin-off series for marketers was inspired by a popular big data presentation I delivered to executives and senior management at a recent SAS Global Forum Executive Conference.


Editor’s note:

If you did not read the previous posts in this series, I encourage you to read those as well. Tamara's goal with this series is to enable you to have an informed view of how this area of technology can support your strategy. Armed with these perspectives, hopefully you can partner even more closely with I.T. and operations to deliver the best possible customer experience.

Once you're comfortable with Hadoop and want to delve deeper into analytically-driven marketing solutions, start with our Customer Intelligence home page at: www.sas.com/customerjourney. And as always, thank you for following!

tags: big data, Big Data Cheat Sheet on Hadoop, customer experience, Hadoop, sas global forum executive conference

Marketers ask: Is Hadoop enterprise-ready? was published on Customer Analytics.

7月 242015
 

"Our corporate data is growing at a rate of 27% each year and we expect that to increase. It’s just getting too expensive to extend and maintain our data warehouse.”

“Don’t talk to us about our ‘big’ data. We’re having enough trouble getting our ‘small’ data processed and analyzed in a timely manner. First things first.”

“We have to keep our data for 7 years for compliance reasons, but we’d love to store and analyze decades of data - without breaking the machine and the bank.”

Do any of these scenarios ring a bell? If so, Hadoop may be able to help. In this 5-part blog series, Big Data Cheat Sheet on Hadoop, we’re taking a look at five big data questions from the perspective of a marketer. This post answers the second question in the series to help marketers understand how these big data technologies are impacting (or can impact) the customer experience, and what you can do to take advantage of this data playground.

Question 2: Why do we need Hadoop if we’re not doing big data?

Contrary to popular belief, Hadoop is not just for big data. (For purposes of this discussion, big data simply refers to data that doesn't fit comfortably – or at all – into your existing relational systems.) Granted, Hadoop was originally developed to address the big data needs of web/media companies, but today, it's being used around the world to address a wider set of data needs, big and small, by practically every industry.

In my white paper, The Non-Geek’s Big Data Playbook: Hadoop and the Enterprise Data Warehouse, I propose six common Hadoop use cases—three of which don’t require “big” data at all to take full advantage of Hadoop:

6 Hadoop Use Cases

Here’s a brief summary of each use case:

  1. Stage structured data. Use Hadoop as a data staging platform for your data warehouse.

What if you used Hadoop to process and transform your operational data before loading it into your data warehouse? The bonus is that because of the low cost of Hadoop storage, you could store both versions of the data in Hadoop: the raw, native data and the transformed data. Your data would now all be in one place, making it easier to manage, re-process, and analyze at a later date.

  1. Process structured data. Use Hadoop to update data in your data warehouse and/or operational systems.

Instead of using costly data warehouse resources to update data in the warehouse, why not send the necessary data to Hadoop, let Hadoop do its thing, and then send the updated data back to the warehouse? This use case not only applies to processing your warehouse data, but also data in any of your operational or analytical systems. Take advantage of Hadoop’s low-cost processing power so that your relational systems are freed up to do what they do best.

  1. Archive all data. Use Hadoop to archive all your data on-premises or in the cloud.

Since Hadoop runs on commodity hardware that scales easily and quickly, organizations can now store and archive a lot more data at a much lower cost. For example, what if you didn’t have to destroy data after its regulatory life to save on storage costs? What if you could easily and cost-effectively keep all your data? Or maybe it’s not just about keeping the data on-hand, but rather, being able to analyze more data. Why limit your analysis to the last three, five or seven years when you can easily store and analyze decades of data? Isn't this a data geek’s paradise?

  1. Process any data. Use Hadoop to take advantage of data that’s currently unavailable in your enterprise data warehouse ecosystem.

This use case focuses on two categories of data: (1) structured data sources that have not been integrated into your data warehouse and (2) unstructured and semi-unstructured data sources. More generally, it’s any data that’s currently not part of your warehouse ecosystem that could be providing additional insight into your customers, products and services. Because Hadoop can store and process any data, it can pick up the slack for data that your data warehouse cannot or doesn’t handle well.

  1. Access any data (via data warehouse). Use Hadoop to extend your data warehouse and keep it at the center of your organization’s data universe.

This use case is geared towards companies that want to keep the enterprise data warehouse as the de facto system of record—at least for now. As a complementary component, Hadoop can be used to process and integrate any type of data—structured, semi-structured, and unstructured—and load what is needed into the data warehouse. This allows companies to continue using their current BI/analytics tools with their enterprise data warehouse ecosystem.

  1. Access any data (via Hadoop). Use Hadoop as the landing platform for all data and exploit the strengths of both the data warehouse and Hadoop.

As mentioned earlier, one advantage of capturing data in Hadoop is that it can be stored in its raw, native state. It does not need to be formatted upfront as with traditional, structured data; it can be formatted at the time of the data request. This use case most closely supports the concept of using Hadoop as a “data lake”—which is a discussion/debate I had recently with a colleague in another forum.

Key takeaways for marketers

Don’t make the mistake of believing that Hadoop is synonymous with big data—because it’s not. It is, however, one of the more popular big data technologies out there that you can use even if you don’t have big data—as pointed out in the first three use cases above. But it’s not just about the technology - this is about enabling you to understand technology enough to understand how it relates to your focus on the customer experience.

Hadoop is here to stay and it’s ready to “play” with your enterprise data warehouse. Download my Non-Geek’s Big Data Playbook to help you figure out which use cases make sense for your organization. This playbook was written for the technologically-savvy business professional who prefers pictures to words, simplicity to complexity, and briefer explanations to longer ones. If this describes you, then what are you waiting for?

This is the 2nd post in a 5-part series, "Big Data Cheat Sheet on Hadoop." This spin-off series for marketers was inspired by a popular big data presentation I delivered to executives and senior management at a recent SAS Global Forum Executive Conference.


Editor’s note:

If you did not read the first post in this series, I encourage you to read that one as well. Tamara's goal with this series is to enable you to have an informed view of how this area of technology can support your strategy. Armed with these perspectives, hopefully you can partner even more closely with I.T. and operations to deliver the best possible customer experience.

Once you're comfortable with Hadoop and want to delve deeper into analytically-driven marketing solutions, start with our Customer Intelligence home page at: www.sas.com/customerjourney. And as always, thank you for following!

tags: big data, Big Data Cheat Sheet on Hadoop, customer experience, Hadoop, sas global forum executive conference

Marketers ask: Why do we need Hadoop if we’re not doing big data? was published on Customer Analytics.

7月 112015
 

Recently, I was given the opportunity to present a session titled, An Executive’s Cheat Sheet on Hadoop, the Enterprise Data Warehouse and the Data Lake at the SAS Global Forum Executive Conference. During this standing-room only session, I addressed these five questions:

  • What can Hadoop do that my data warehouse can’t?
  • We’re not doing “big” data, so why do we need Hadoop?
  • Is Hadoop enterprise-ready?
  • Isn’t a data lake just the data warehouse revisited?
  • What are some of the pros and cons of a data lake?

I've been inspired to re-think  my answers to those 5 questions in terms of the customer experience and present them for marketers as a 5-part series in this blog. My goal is to help marketers understand how these big data technologies are impacting (or can impact) the customer experience, and what you can do to take advantage of this data playground. Let’s get started!

Question 1: What can Hadoop do that my data warehouse can’t?

Here’s the short answer: (1) Store any and all kinds of data more cheaply and (2) process all this data more quickly and cheaply.

The longer answer is:
[Please excuse me as I step up on one of my big data soapboxes to address this question.]

I’m here to tell you that big data is not new. Yet, with all the hype these last few years around these two little words, you’d think we’ve discovered the Holy Grail. Let me share with you the dirty little secret about big data: it’s just data—the same data we’ve had for decades.

Big data is not new

They say that 20% of the data we deal with today is structured data (see examples in orange boxes above). I also call this traditional, relational data. The other 80% is semi-structured or unstructured data (examples in blue boxes), and this is what I call “big” data.

Are any of these blue-box data types new? Of course not. We’ve been collecting, processing, storing, and analyzing all this data for decades. What we haven’t been able to do very well, however, if at all, is mix the orange- and blue-box data together.

So here’s what’s new: We now have the technologies to collect, process, store, and analyze all this data together. In other words, with Hadoop, we can now mix-&-match the orange- and blue-box data together – at a fraction of the cost and time of our traditional, relational systems. You can’t do that with your data warehouse.
[I’m stepping off my soapbox now.]

Why this matters

Big data technologies like Hadoop take the “360-degree view of the customer” concept to a whole new level. Let’s say you want to provide your customers with an omnichannel experience, so that no matter how they choose to interact with you, you’re right there with them. It’s possible with data. The diagram above includes 25 sample data sources, many of which contain customer data. What if you could tie these data sources together to provide your customer with a satisfying and even fun experience?

Consider this scenario: One of your loyal customers posts on Facebook that she’s going shopping at one of your stores today. You know that she just purchased a pair of pants online last week, and that her abandoned online shopping cart has a few cute tops in it to go with the pants. She goes to the store, the retail assistant is able to identify who she is and brings out the tops she abandoned online to try on with her new pants. But since your customer isn’t wearing her new pants, the retail assistant knows which size pants to go grab. Then while shopping, your customer gets a 25% off coupon delivered to her smartphone—good for today only.

All creepiness aside, this retail scenario is not as far-fetched as you may think. This is what mixing-&-matching your customer data will allow you to do. With Hadoop, not your data warehouse.

Key takeaways for marketers

Before you go bust down IT’s door and ask them to install Hadoop so that you can have a better 360-degree view of your customers, please understand that this is easier said than done. Whereby these big data technologies make mixing-&-matching your data possible (which is a huge feat in itself!), be aware that the tools themselves are still maturing. You will need technical assistancefrom IT and developers, internally and externallyto get started with Hadoop.

But it’s not just about the technology. I strongly encourage you to follow these three steps if you want to be successful with Hadoop:

  • Identify the business issue. Don’t “do Hadoop” just because everyone else is doing it or because it looks good on paper or it’s cheap to install. Do Hadoop if it helps address or solve a real business issue for your organization.
  • Get executive buy-in before—not after—you get started. Don’t embark on a big data project without executive support. Even successful projects have been shot down because they couldn’t get executive support and/or they didn’t support corporate strategies.
  • Develop a multi-player plan. Don’t do Hadoop, or big data for that matter, alone. It’s not a single department play. Big data projects require multiple players from the business, IT, and executive management.

Many companies eager to jump on the Hadoop bandwagon have missed these three steps, and guess what they have to show for it now? Abandoned Hadoop installations.

Don’t be one of those companies.

 This is the 1st post in a 5-part series, “A Big Data Cheat Sheet: What Marketers Want to Know.” This spin-off series for marketers was inspired by a popular big data presentation I delivered to executives and senior management at the SAS Global Forum Executive Conference earlier this year.


Editor’s note:

Tamara's ability to make technology accessible to marketers is what makes her perspective so valuable to this blog. And my favorite part about her message is that marketers don't need to be experts in Hadoop to effectively harness the potential in big data. The key is to know just enough about Hadoop so you can have an informed discussion with your technical counterparts about meeting your business needs. The bottom line is that big data technologies such as Hadoop can indeed help marketers deliver a better customer experience.

For a little more detail about Hadoop, I'd recommend this paper by the International Institute for Analytics: The Current State of Hadoop in the Enterprise. Once you're comfortable with Hadoop and want to delve deeper into analytically-driven marketing solutions, start with our Customer Intelligence home page at: www.sas.com/customerjourney.

tags: big data, customer experience, Hadoop, sas global forum executive conference

Marketers ask: What can Hadoop do that my data warehouse can’t? was published on Customer Analytics.

6月 102015
 

How is it that some companies can come up with a big idea and implement that idea successfully in the market, while others never get past the idea phase? "In the case of innovation," says Jill Dyché, VP of SAS Best Practices, "big ideas aren't enough." It's also not enough […]

The post The pros and cons of innovation labs appeared first on SAS Voices.

4月 292015
 

We’ve all heard the old saw, “If you torture data long enough, eventually it will confess to something.” But when it comes to spurring real change, how about ditching the dungeon-master act and thinking like a venture capitalist instead? Wouldn’t that pay bigger dividends? That was the tip from Ravi […]

The post Data scientist as venture capitalist appeared first on SAS Voices.

4月 292015
 

Texas-based multinational ConocoPhillips doesn’t do anything on a small scale. When the energy company thinks about projects, those projects typically cost many billions of dollars. So when it comes to identifying where new sources of oil and gas reside, getting it wrong is not an option. The company also has […]

The post ConocoPhillips taps the analytics motherlode to find Texas tea appeared first on SAS Voices.

4月 012014
 
Judging by the headlines like “Big Data Sparks Corporate Turf Fights” and “5 Things CFOs Hate About IT,” you might think that every IT organization is at odds with the company’s business leaders. But let me ask you, does this look like a group of people at odds with one […]
3月 272014
 

It’s never been more important for marketing to speak the language of technology (IT) and for IT to speak the language of marketing. Why? Because technology is radically changing the world and marketing and IT have the opportunity to radically change the enterprise together. Yes, the stakes are high and so is the potential payoff.

The opportunity is to create great value by realizing the innovative power of technology, and doing it requires agility, resourcefulness and a willingness to collaborate like never before.  The interesting part is that each of those three factors rely heavily on people skills.

SAS Global Forum Executive Conference Keynote Panel Discussion

Keynote Panel Discussion, SAS Global Forum Executive Conference

These themes emerged among the topics covered by the keynote panel at SAS Global Forum Executive Conference and it struck me how much they also resonate in marketing.  The panel was moderated by SAS Vice President of Best Practices Jill Dyché and included these executives:

Jill set up the discussion with the idea that “we’re still victims of the cultural paradigms at our companies” – and she cited examples to underscore her point. There are legacy mind-sets and processes that need to be changed before the possibilities can take place, and that’s why the catalysts are the people skills. It’s natural to have a viewpoint informed by past experience, and getting beyond those predispositions takes work. It’s not too different than going to a family reunion or when visiting your home town – it may take some adjusting for your old neighbors to see beyond your teenage years’ antics and engage with you as the adult you’ve become. Showing up with your wife and kids helps that process, but it still takes time.

Overcoming Legacy Mindets
Peter noted the legacy mindset that evolved because IT has traditionally had control over what happened and why – largely in siloes. Now, we have consumer-driven collaboration and related applications that are impacting the way things are happening.  His point was that the only way to drive technology-driven innovation is to seamlessly connect customers and employees back to your systems of record (CRM, ERP, etc), and it’s actually more accurate to consider them “systems of engagement.” Not making those connections, of course, means potentially forgoing the opportunity to drive the engagement.

The corollary in marketing is the heritage of push-marketing and campaigns that make it seem like you’re always broadcasting and not listening. Customers want something different nowadays and they want to engage with your organization on their terms – and listening is not optional. Opt-outs and do-not-call-lists are proof positive that marketers not only have to be sure their message is relevant, but so are timing, channel and even medium. Think of how frustrating it is to get an offer via email on your smartphone and then be taken to a non-mobile friendly page.

Attitude and Approach Matter
Attitude and approach also have a big impact. Mary, whose current role as CEO of Canadian Tire includes both IT and marketing, has visibility to both sides. Her view is that if IT comes to the table thinking that they’re just accountable for getting things done and if they don’t see that they can bring the future to fruition, that’s a problem.  Another big issue is language – not having a common language is an impediment.  Coming together to identify common challenges is very important, especially when it’s clear how each party brings a critical piece of the puzzle to the solution.

Find a Common Language
A recurring theme throughout the discussion involved language and perspective. Jill noted that she still has occasions when talking with IT executives that they are referring to their business counterparts as “they,” invoking the classic “us versus them” point of view.  Language also comes to bear in standardization initiatives, which often happen on an enterprisewide basis. Those can be especially threatening because standardizing can often be seen as involving some degree of giving up control. So when peoples’ accountability doesn’t change accordingly, the threat can seem quite real. James noted that it’s often best to start by simply agreeing on definitions to frame the discussion with everyone having the same understanding. Process changes and standardization in marketing are happening increasingly with IT at the same table, so this idea of needing a common language has never been more critical.

Get a New Perspective
A change in perspective can be transformative. James shared that one of the most impactful things for him as a CIO was an invitation from one of business counterparts.  A major business unit head took him on a sales call, during which he got insights into the customer he would never have gotten in a conference room. And those revelations gave him valuable common ground with his business counterparts that evolved into a very productive partnership.

And sometimes a major technology initiative can spring forth quite far from headquarters, which is a whole different take on perspective.  That was the case when FedEx gave its drivers hand-held scanners a decade or so ago. Back then, it was a competitive advantage that was initiated in their field operations in response to service issues.  Today it’s table stakes for any package handling operation. The changes driven by shifting customer behaviors and needs are happening quickly, and collaboration is increasingly needed to realize the potential of technology to address those needs.

Reinvention – the Ultimate in Adaptability
Jill cited a recent ComputerWorld article that spoke to the thought that the best IT leaders reinvent themselves, which led to James sharing his experiences in reinventing himself. In most companies where he supported the business from an IT standpoint, he wound up running the business. And that had to do with how he reinvented himself – which he’s done in 3 ways at different times:

  1. He went from being a speaker to a communicator.  As a speaker, he spoke the language of IT, but as a communicator he spoke the language of business. Most business leaders do not have a quantitative background, so making the change from a speaker to a communicator is to get out there and get to know the business.
  2. He went from being a project manager to a change manager. As a project manager, he was focused on the technology and the tasks, and as a change manager he was more focused on the people and the processes and how the technology is going to impact them.
  3. He went from being a student of risk management to a student of critical thinking, where he evolved from focusing on controlling risks identified in assessments to more thoughtfully assessing the hidden biases that were feeding into the very assessments. It became apparent over time that the issue often was not that the risk were unidentified, it’s that they simply weren’t assessed properly.

And related to critical thinking is getting the right perspective when faced with big data. The more data you have, the more noise you’ll have so you need to know how to tune out the noise. And it also helps to remember that data is objective and interpretation is subjective. Someone will be interpreting that data, and it’s critical to understand the thinking that went into the models that are built to deliver the insights needed to make informed decisions. By knowing the underlying thought processes, you know the key levers that the company needs to be successful and how to pull those levers.

And knowing the right levers and how to marshal resources across organizational lines to respond to technology changes are what it takes for executives to drive innovation. Finding a common language is a stepping-stone to finding common ground for collaboration. And changes in attitudes and perspectives will go a long way toward leaving legacy mindsets behind to enable customer-centered strategies that add lasting value. We’re all facing technology changes – and we should all consider how they are affecting how we operate, and what the changes we need to be a part of making.

Or as James observed in a closing thought:

Most leaders don’t fail at the “what to do” question – it’s usually “how we do it” that trips people up.

tags: big data, CIO, CMO, leadership, sas global forum executive conference, strategy