Category Archives: CIO
I’ve spent most of my career working with new technology, most recently helping companies make sense of mountains of incoming data. This means, as I like to tell people, that I have the sexiest job in the 21st century.
Harvard Business Review put the data scientist into the national spotlight in their publication Data Scientist: The Sexiest Job of the 21st Century. Job trends data from Indeed.com confirms the rise in popularity for the position, showing that the number of job postings for data scientist positions increased by 15,000%.
In the meantime, the role of data scientist has changed dramatically. Data used to reside on the fringes of the operation. It was usually important but seldom vital – a dreary task reserved for the geekiest of the geeks. It supported every function but never seemed to lead them. Even the executives who respected it never quite absorbed it.
For every Big Data problem, the solution often rests on the shoulders of a data scientist. The role of the data scientist is similar in responsibility to the Wall Street “quants” of the 80s and 90s – now, these data experienced are tasked with the management of databases previously thought too hard to handle, and too unstructured to derive any value.
So, is it the sexiest job of the 21st Century?
Think of a data scientist more like the business analyst-plus, part mathematician, part business strategist, these statistical savants are able to apply their background in mathematics to help companies tame their data dragons. But these individuals aren’t just math geeks, per se.
A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a renaissance individual who really wants to learn and bring change to an organization.
If this sounds like you, the good news is demand for data scientists is far outstripping supply. Nonetheless, with the rising popularity of the data scientist – not to mention the companies that are hiring for these positions – you have to be at the top of your field to get the jobs.
Companies look to build teams around data scientists that ask the most questions about:
- How the business works
- How it collects its data
- How it intends to use this data
- What it hopes to achieve from these analyses
These questions were important because data scientists will often unearth information that can “reshape an entire company.” Obtaining a better understanding of the business’ underpinnings not only directs the data scientist’s research, but helps them present the findings and communicate with the less-analytical executives within the organization.
While it’s important to understand your own business, learning about the successes of other corporations will help a data scientist in their current job–and the next.
Talking to architects about analytics at a recent event, I kept hearing the familiar theme; data scientists are spending 80% of their time on “data wrangling” leaving only 20% for delivering the business insights that will drive the company’s innovation. It was clear to everybody that I spoke to that the situation will only worsen. The coming growth everybody sees in data volume and complexity, will only lengthen the time to value.
Gartner recently predicted that:
“by 2015, 50% of organizations will give up on managing growth and will redirect funds to improve classification and analytics.”
Some of the details of this study are interesting. In the end, many organizations are coming to two conclusions:
- It’s risky to delete data, so they keep it around as insurance.
- All data has potential business value, so more organizations are keeping it around for potential analytical purposes.
The other mega-trend here is that more and more organizations are looking to compete on analytics – and they need data to do it, both internal data and external data.
From an architect’s perspective, here are several observations:
- The floodgates are open and analytics is a top priority. Given that, the emphasis should be on architecting to manage the dramatic increases in both data quantity and data complexity rather than on trying to stop it.
- The immediate architectural priority has to be on simplifying and streamlining your current enterprise data architecture. Break down those data silos and standardize your enterprise data management tools and processes as much as possible. As discussed in other blogs, data integration is becoming the biggest bottleneck to business value delivery in your environment. Gartner has projected that “by 2018, more than half the cost of implementing new large systems will be spent on integration.” The more standardized your enterprise data management architecture is, the more efficient it will be.
- With each new data type, new data tool (Hive, Pig, etc.), and new data storage technology (Hadoop, NoSQL, etc.) ask first if your existing enterprise data management tools can handle the task before people go out and create a new “data silo” based on the cool, new technologies. Sometimes it will be necessary, but not always.
- The focus needs to be on speeding value delivery for the business. And the key bottleneck is highly likely to be your enterprise data architecture.
Rather than focusing on managing data growth, the priority should be on managing it in the most standardized and efficient way possible. It is time to think about enterprise data management as a function with standard processes, skills and tools (just like Finance, Marketing or Procurement.)
Several of our leading customers have built or are building a central “Data as a Service” platform within their organizations. This is a single, central place where all developers and analysts can go to get trustworthy data that is managed by IT through a standard architecture and served up for use by all.
For more information, see “The Big Big Data Workbook”
*Gartner Predicts 2015: Managing ‘Data Lakes’ of Unprecedented Enormity, December 2014 http://www.gartner.com/document/2934417#
Customers often inquire about the best way to get their team up to speed on the Informatica solutions. The question Informatica University hears frequently is whether a team should attend our public scheduled courses or hold a Private training event. The number of resources to be skilled on the products will help to determine which option to choose. If your team, or multiple teams within your company, has 7 or more resources that require getting up to speed on the Informatica products, then a Private training event is the recommended choice.
Seven (7) for a remote instructor and nine (9) for an onsite instructor is the break even cost per resource when determining whether to hold a private training and is the most cost efficient delivery for a team. In addition to the cost benefit, customers who have taken this option value the daily access to their team members to keep business operations humming along, and the opportunity to collaborate with key team members not attending by allowing them to provide input to project perspective.
These reserved events also provide the opportunity to be adapted to focus on a customers needs by tailoring course materials to highlight topics that will be key to a project’s implementation which provide creative options to get a team up to speed on the Informatica projects at hand.
With Informatica University’s new flexible pricing, hosting a Private Training event is easy. All it takes is:
- A conference room
- Training PC’s or laptops for participants
- Access to the Internet
- An LCD projector, screen, white board, and appropriate markers
Private training events provide the opportunity to get your resources comfortable and efficient with the Informatica Solutions and have a positive impact on the success of your projects.
To understand more about Informatica’s New Flexible Pricing, contact email@example.com
A Data Lake is a simple concept. They are a catchment area for data entering the organization. In the past, most businesses didn’t need to organize such a data store because almost all data was internal. It traveled via traditional ETL mechanisms from transactional systems to a data warehouse and then was sprayed around the business, as required.
When a good deal of data comes from external sources, or even from internal sources like log files, which never previously made it into the data warehouse, there is a need for an “operational data store.” This has definitely become the premier application for Hadoop and it makes perfect sense to me that such technology be used for a data catchment area. The neat thing about Hadoop for this application is that:
- It scales out “as far as the eye can see,” so there’s no likelihood of it being unable to manage the data volumes even when they grow beyond the petabyte level.
- It is a key-value store, which means that you don’t need to expend much effort in modeling data when you decide to accommodate a new data source. You just define a key and define the metadata at leisure.
- The cost of the software and the storage is very low.
So let’s imagine that we have a need for a data catchment area, because we have decided to collect data from log-files, mobile devices, social networks, from public data sources, or whatever. So let us also imagine that we have implemented Hadoop and some of its useful components and we have begun to collect data.
Is it reasonable to describe this as a data lake?
A Hadoop implementation should not be a set of servers randomly placed at the confluence of various data flows. The placement needs to be carefully considered and if the implementation is to resemble a “data lake” in any way, then it must be a well-engineered man-made lake. Since the data doesn’t just sit there until it evaporates but eventually flows to various applications, we should think of this as a “data reservoir” rather than a “data lake.”
There is no point in arranging all that data neatly along the aisles because when we get it, we may not know what we want to do with it at the time we get it. We should organize the data when we know that.
Another reason we should think of this as more like a reservoir than a lake is that we might like to purify the data a little before sending it down the pipes to applications or users that want to use it.
The start of the year is a great time to refresh and take a new look at your capabilities, goals, and plans for your future-state architecture. That being said, you have to take into consideration that the most scarce resource in your architecture is probably your own personal time.
Looking forward, here are three things that I would recommend that every architect do. I realize that all three of these relate to data, but as I have said in the eBook, Think “Data First” to Drive Business Value, we believe that data is the key bottleneck in your enterprise architecture in terms of slowing the delivery of business initiatives in support of your organization’s business strategy.
So, here are the recommendations. None of these will cost you anything if you are a current Informatica PowerCenter customer. And #2 and #3 are free regardless. It is only a matter of your time:
1. Take a look at the current Informatica Cloud offering and in particular the templating capabilities.
Informatica Cloud is probably much more capable than you think. The standard templating functionality supports very complex use cases and does it all from a very easy to use, no-coding, user interface. It comes with a strong library of integration stubs that can be dragged & dropped into Microsoft Viseo to create complex integrations. Once the flow is designed in Viseo, it can be easily imported into Informatica Cloud and from there users have a Wizard-driven UI to do the final customization for sources, targets, mappings, transformations, filters, etc. It is all very powerful and easy to use.
- YouTube: Building Custom templates https://www.youtube.com/watch?v=yHmFkxov6bs
- 30 day free Informatica Cloud trial. http://more.informatica.com/en/cloud_trial/org?offer=30day-ICwebPage
Why This Matters to Architects
- You will see how easy it is for new groups to get going with fairly complex integrations.
- This is a great tool for departmental or new user use, and it will be completely compatible with the rest of your Informatica architecture – not another technology silo for you to manage.
- Any mapping created for Informatica on-premise can also run on the cloud version.
2. Download Informatica Rev and understand what it can do for your analysts and “data wranglers.”
Your data analysts are spending 80% of their time managing their data and only 20% on the actual analysis they are trying to provide. Informatica Rev is a great way to prepare your data before use in analytics tools such as Qlik, Tableau, and others.
With Informatica Rev, people who are not data experts can access, mashup, prototype and cleanse their data all in a User Interface that looks like a spreadsheet and requires no previous experience in data tools.
- For a free Informatica Rev download https://rev.informatica.com/
- Informatica Rev (Project Springbok) demo https://www.youtube.com/watch?v=0F_58bHKDDs
Why This Matters for Architects
- Your data analysts are going to use analytics tools with or without the help of IT. This enables you to help them while ensuring that they are managing their data well and optimizing their productivity.
- This tool will also enable them to share their “data recipes” and for IT to be involved in how they access and use the organization’s data.
3. Look at the new features in PowerCenter 9.6. First, upgrade to 9.6 if you haven’t already, and particularly take a good look at these new capabilities that are bundled in every version. Many people we talk to have 9.6 but don’t realize the power of what they already own.
- Profiling: Discover and analyze your data quickly. Find relationships and data issues.
- Data Services: This presents any JDBC or ODBC repository as a logical data object. From there you can rapidly prototype new applications using these logical objects without worrying about the complexities of the underlying repositories. It can also do data cleansing on the fly.
- Webinar: Great Data by Design. https://www.brighttalk.com/webcast/10477/104939
- PowerCenter 9.6 deep dive demo https://www.brighttalk.com/webcast/10477/110535
Why This Matters for Architects
- The key challenge for IT and for Architects is to be able to deliver at the “speed of business.” These tools can dramatically improve the productivity of your team and speed the delivery of projects for your business “customers.”
Taking the time to understand what these tools can do in terms of increasing the productivity of your IT team and enabling your end users to self-service will make you a better business partner overall and increase your influence across the organization. Have a great year!
The thing that resonates today, in the odd context of big data, is that we may all need to look in the mirror, hold a thumb drive full of information in our hands, and concede once and for all It’s not the data… it’s us.
Many organizations have a hard time making something useful from the ever-expanding universe of big-data, but the problem doesn’t lie with the data: It’s a people problem.
The contention is that big-data is falling short of the hype because people are:
- too unwilling to create cultures that value standardized, efficient, and repeatable information, and
- too complex to be reduced to “thin data” created from digital traces.
Evan Stubbs describes poor data quality as the data analyst’s single greatest problem.
About the only satisfying thing about having bad data is the schadenfreude that goes along with it. There’s cold solace in knowing that regardless of how poor your data is, everyone else’s is equally as bad. The thing is poor quality data doesn’t just appear from the ether. It’s created. Leave the dirty dishes for long enough and you’ll end up with cockroaches and cholera. Ignore data quality and eventually you’ll have black holes of untrustworthy information. Here’s the hard truth: we’re the reason bad data exists.
I will tell you that most data teams make “large efforts” to scrub their data. Those “infrequent” big cleanups however only treat the symptom, not the cause – and ultimately lead to inefficiency, cost, and even more frustration.
It’s intuitive and natural to think that data quality is a technological problem. It’s not; it’s a cultural problem. The real answer is that you need to create a culture that values standardized, efficient, and repeatable information.
If you do that, then you’ll be able to create data that is re-usable, efficient, and high quality. Rather than trying to manage a shanty of half-baked source tables, effective teams put the effort into designing, maintaining, and documenting their data. Instead of being a one-off activity, it becomes part of business as usual, something that’s simply part of daily life.
However, even if that data is the best it can possibly be, is it even capable of delivering on the big-data promise of greater insights about things like the habits, needs, and desires of customers?
Despite the enormous growth of data and the success of a few companies like Amazon and Netflix, “the reality is that deeper insights for most organizations remain elusive,” write Mikkel Rasmussen and Christian Madsbjerg in a Bloomberg Businessweek blog post that argues “big-data gets people wrong.”
Big-data delivers thin data. In the social sciences, we distinguish between two types of human behavior data. The first – thin data – is from digital traces: He wears a size 8, has blue eyes, and drinks pinot noir. The second – rich data – delivers an understanding of how people actually experience the world: He could smell the grass after the rain, he looked at her in that special way, and the new running shoes made him look faster. Big-data focuses solely on correlation, paying no attention to causality. What good is thin “information” when there is no insight into what your consumers actually think and feel?
Accenture reported only 20 percent of the companies it profiled had found a proven causal link between “what they measure and the outcomes they are intending to drive.”
Now, I can contend they keys to transforming big-data to strategic value are critical thinking skills.
Where do we get such skills? People, it seems, are both the problem and the solution. Are we failing on two fronts: failing to create the right data-driven cultures, and failing to interpret the data we collect?
The current trend is that new types of data and new types of physical storage are changing all of that.
When I got back from my trip I found a TDWI white paper by Philip Russom that describes the situation very well in a white paper detailing his research on this subject; Evolving Data Warehouse Architectures in the Age of Big Data.
From an enterprise data architecture and management point of view, this is a very interesting paper.
- First the DW architectures are getting complex because of all the new physical storage options available
- Hadoop – very large scale and inexpensive
- NoSQL DBMS – beyond tabular data
- Columnar DBMS – very fast seek time
- DW Appliances – very fast / very expensive
- What is driving these changes is the rapidly-increasing complexity of data. Data volume has captured the imagination of the press, but it is really the rising complexity of the data types that is going to challenge architects.
- But, here is what really jumped out at me. When they asked the people in their survey what are the important components of their data warehouse architecture, the answer came back; Standards and rules. Specifically, they meant how data is modeled, how data quality metrics are created, metadata requirements, interfaces for data integration, etc.
The conclusion for me, from this part of the survey, was that business strategy is requiring more complex data for better analyses (example: realtime response or proactive recommendations) and business processes (example: advanced customer service). This, in turn, is driving IT to look into more advanced technology to deal with different data types and different use cases for the data. And finally, the way they are dealing with the exploding complexity was through standards, particularly data standards. If you are dealing with increasing complexity and have to do it better, faster and cheaper, they only way you are going to survive is by standardizing as much as reasonably makes sense. But, not a bit more.
If you think about it, it is good advice. Get your data standards in place first. It is the best way to manage the data and technology complexity. …And a chance to be the driver rather than the driven.
I highly recommend reading this white paper. There is far more in it than I can cover here. There is also a Philip Russom webinar on DW Architecture that I recommend.
A month ago, I shared that Frank Friedman believes CFOs are “the logical choice to own analytics and put them to work to serve the organization’s needs”. Even though many CFOs are increasingly taking on what could be considered an internal CEO or COO role, many readers protested my post which focused on reviewing Frank Friedman’s argument. At the same time, CIOs have been very clear with me that they do not want to personally become their company’s data steward. So the question becomes should companies be creating a CDO or CAO role to lead this important function? And if yes, how common are these two roles anyway?
Regardless of eventual ownership, extracting value out of data is becoming a critical business capability. It is clear that data scientists should not be shoe horned into the traditional business analyst role. Data Scientists have the unique ability to derive mathematical models “for the extraction of knowledge from data “(Data Science for Business, Foster Provost, 2013, pg 2). For this reason, Thomas Davenport claims that data scientists need to be able to network across an entire business and be able to work at the intersection of business goals, constraints, processes, available data and analytical possibilities. Given this, many organizations today are starting to experiment with the notion of having either a chief data officers (CDOs) or chief analytics officers (CAOs). The open questions is should an enterprise have a CDO or a CAO or both? And as important in the end, it is important to determine where should each of these roles report in the organization?
Data policy versus business questions
In my opinion, it is the critical to first look into the substance of each role before making a decision with regards to the above question. The CDO should be about ensuring that information is properly secured, stored, transmitted or destroyed. This includes, according to COBIT 5, that there are effective security and controls over information systems. To do this, procedures need to be defined and implemented to ensure the integrity and consistency of information stored in databases, data warehouses, and data archives. According to COBIT 5, data governance requires the following four elements:
- Clear information ownership
- Timely, correct information
- Clear enterprise architecture and efficiency
- Compliance and security
To me, these four elements should be the essence of the CDO role. Having said this, the CAO is related but very different in terms of the nature of the role and the business skills require. The CRISP model points out just how different the two roles are. According to CRISP, the CAO role should be focused upon business understanding, data understanding, data preparation, data modeling, and data evaluation. As such the CAO is focused upon using data to solve business problems while the CDO is about protecting data as a business critical asset. I was living in in Silicon Valley during the “Internet Bust”. I remember seeing very few job descriptions and few job descriptions that existed said that they wanted a developer who could also act as a product manager and do some marketing as a part time activity. This of course made no sense. I feel the same way about the idea of combining the CDO and CAO. One is about compliance and protecting data and the other is about solving business problems with data. Peanut butter and chocolate may work in a Reese’s cup but it will not work here—the orientations are too different.
So which business leader should own the CDO and CAO?
Clearly, having two more C’s in the C-Suite creates a more crowded list of corporate officers. Some have even said that this will extended what is called senior executive bloat. And what of course how do these new roles work with and impact the CIO? The answer depends on organization’s culture, of course. However, where there isn’t an executive staff office, I suggest that these roles go to different places. Clearly, many companies already have their CIO function already reporting to finance. Where this is the case, it is important determine whether a COO function is in place. The COO clearly could own the CDO and CAO functions because they have a significant role in improving process processes and capabilities. Where there isn’t a COO function and the CIO reports to the CEO, I think you could have the CDO report to the CIO even though CIOs say they do not want to be a data steward. This could be a third function in parallel the VP of Ops and VP of Apps. And in this case, I would put the CAO report to one of the following: the CFO, Strategy, or IT. Again this all depends on current organizational structure and corporate culture. Regardless of where it reports, the important thing is to focus the CAO on an enterprise analytics capability.
Author Twitter: @MylesSuer
According Michelle Fox of CNBC and Stephen Schork, the oil industry is in ‘dire straits’. U.S. crude posted its ninth-straight weekly loss this week, landing under $50 a barrel. The news is bad enough that it is now expected to lead to major job losses. The Dallas Federal Reserve anticipates that the Texas could lose about 125,000 jobs by the end of June. Patrick Jankowski, an economist and vice president of research at the Greater Houston Partnership, expects exploration budgets will be cut 30-35 percent, which will result in approximately 9,000 fewer wells being drilled. The problem is “if oil prices keep falling, at some point it’s not profitable to pull it out of the ground” (“When, and where, oil is too cheap to be profitable”, CNBC, John W. Schoen).
This means that a portion of the world’s oil supply will become unprofitable to produce. According to Wood Mackenzie, “once the oil price reaches these levels, producers have a sometimes complex decision to continue producing, losing money on every barrel produced, or to halt production, which will reduce supply”. The question are these the only answers?
Major Oil Company Uses Analytics to Gain Business Advantage
A major oil company that we are working with has determined that data is a success enabler for their business. They are demonstrating what we at Informatica like to call a “data ready business”—a business that is ready for any change in market conditions. This company is using next generation analytics to ensure their businesses survival and to make sure they do not become what Jim Cramer likes to call a “marginal producer”. This company has said to us that their success is based upon being able to extract oil more efficiently than its competitors.
Historically data analysis was pretty simple
Traditionally oil producers would get oil by drilling a new hole in the ground. And in 6 months they would start getting the oil flowing commercially and be in business. This meant it would typically take them 6 months or longer before they could get any meaningful results including data that could be used to make broader production decisions.
Drilling from data
Today, oil is, also, produced from shale or fracking techniques. This process can take only 30-60 days before oil producers start seeing results. It is based not just on innovation in the refining of oil, but also on innovation in the refining of data from operational business decisions can be made. The benefits of this approach including the following:
Improved fracking process efficiency
Fracking is a very technical process. Producers can have two wells on the same field that are performing at very different levels of efficiency. To address this issue, the oil company that we have been discussing throughout this piece is using real-time data to optimize its oil extraction across an entire oil field or region. Insights derived from these allow them to compare wells in the same region for efficiency or productivity and even switch off certain wells if the oil price drops below profitability thresholds. This ability is especially important as the price of oil continues to drop. At $70/barrel, many operators go into the red while more efficient data driven operators can remain profitable at $40/barrel. So efficiency is critical across a system of wells.
Using data to decide where to build wells in the first place
When constructing a fracking or sands well, you need more information on trends and formulas to extract oil from the ground. On a site with 100+ wells for example, each one is slightly different because of water tables, ground structure, and the details of the geography. You need the right data, the right formula, and the right method to extract the oil at the best price and not impact the environment at the very same time.
The right technology delivers the needed business advantage
Of course, technology is never been simple to implement. The company we are discussing has 1.2 Petabytes of data they were processing and this volume is only increasing. They are running fiber optic cables down into wells to gather data in real time. As a result, they are receiving vast amounts of real time data but cannot store and analyze the volume of data efficiently in conventional systems. Meanwhile, the time to aggregate and run reports can miss the window of opportunity while increasing cost. Making matters worse, this company had a lot of different varieties of data. It also turns out that quite of bit of the useful information in their data sets was in the comments section of their source application. So traditional data warehousing would not help them to extract the information they really need. They decided to move to new technology, Hadoop. But even seemingly simple problems, like getting access to data were an issue within Hadoop. If you didn’t know the right data analyst, you might not get the data you needed in a timely fashion. Compounding things, a lack of Hadoop skills in Oklahoma proved to be a real problem.
The right technology delivers the right capability
The company had been using a traditional data warehousing environment for years. But they needed help to deal with their Hadoop environment. This meant dealing with the volume, variety and quality of their source well data. They needed a safe, efficient way to integrate all types of data on Hadoop at any scale without having to learn the internals of Hadoop. Early adopters of Hadoop and other Big Data technologies have had no choice but to hand-code using Java or scripting languages such as Pig or Hive. Hiring and retaining big data experts proved time consuming and costly. This is because data scientists and analysts can spend only 20 percent of their time on data analysis and the rest on the tedious mechanics of data integration such as accessing, parsing, and managing data. Fortunately for this oil producer, it didn’t have to be this way. They were able to get away with none of the specialized coding required to scale performance on distributed computing platforms like Hadoop. Additionally, they were able “Map Once, Deploy Anywhere,” knowing that even as technologies change they can run data integration jobs without having to rebuild data processing flows.
It seems clear that we live in an era where data is at the center of just about every business. Data-ready enterprises are able to adapt and win regardless of changing market conditions. These businesses invested in building their enterprise analytics capability before market conditions change. In this case, these oil producers will be able to produce oil at lower costs than others within their industry. Analytics provides three benefits to oil refiners.
- Better margins and lower costs from operations
- Lowers risk of environmental impact
- Lower time to build a successful well
In essence, those that build analytics as a core enterprise capability will continue to have a right to win within a dynamic oil pricing environment.
Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Like me, you probably just returned from an inspiring Sales Kick Off 2015 event. You’ve invested in talented people. You’ve trained them with the skills and knowledge they need to identify, qualify, validate, negotiate and close deals. You’ve invested in world-class applications, like Salesforce Sales Cloud, to empower your sales team to sell more effectively. But does your sales team have what they need to succeed in 2015?
Gartner predicts that as early as next year, companies will compete primarily on the customer experiences they deliver. So, every customer interaction counts. Knowing your customers is key to delivering great sales experiences.
But, inaccurate, inconsistent and disconnected customer information may be holding your sales team back from delivering great sales experiences. If you’re not fueling Salesforce Sales Cloud (or another Sales Force Automation (SFA) application) with clean, consistent and connected customer information, your sales team may be at a disadvantage against the competition.
To successfully compete and deliver great sales experiences more efficiently, your sales team needs a complete picture of their customers. They don’t want to pull information from multiple applications and then reconcile it in spreadsheets. They want direct access to the Total Customer Relationship across channels, touch points and products within their Salesforce Sales Cloud.
Watch this short video comparing a day-in-the-life of two sales reps competing for the same business. One has access to the Total Customer Relationship in Salesforce Sales Cloud, the other does not. Watch now: Salesforce.com with Clean, Consistent and Connected Customer Information.
Is your sales team spending time creating spreadsheets by pulling together customer information from multiple applications and then reconciling it to understand the Total Customer Relationship across channels, touch points and products? If so, how much is it costing your business? Or is your sales team engaging with customers without understanding the Total Customer Relationship? How much is that costing your business?
Many innovative sales leaders are gaining a competitive edge by better leveraging their customer data to empower their sales teams to deliver great sales experiences. They are fueling business and analytical applications, like Salesforce Sales Cloud, with clean, consistent and connected customer information. They are arming their sales teams with direct access to richer customer profiles, which includes the Total Customer Relationship across channels, touch points and products.
What measurable results have these sales leaders acheived? Merrill Lynch boosted sales productivity by 15%, resulting in $50M in annual impact. A $60B manufacturing company improved cross-sell and up-sell success by 5%. Logitech increased across channels: online, in their retail partner’s stores and through distribution partners.
This year, I believe more sales leaders will focus on leveraging their customer information for competitive advantage. This will help them shift from sales automation to sales optimization. What do you think?