Category Archives: Architects
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#
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!
Knowing business’s trends and needs change frequently, why is it that we plan multi-year IT-driven roadmaps?
Understandably, IT managers have honed their skills in working with the line to predict business needs. They have learned to spend money and time wisely and to have the right infrastructure in place to meet the business’ needs. Whether it is launching in a new market, implementing a new technology, or one of many other areas where IT can help its firm find a competitive advantage.
Not so long ago, IT was so complex and unwieldy that it needed specially-trained professionals to source, build, and run almost every aspect of it, and when line managers had scant understanding which technology would suit their activities best, making a plan based on long-term business goals was a good one.
Today, we talk of IT as a utility, just like electricity, you press a button, and IT turns “on.” However that is not the case, the extent to which IT has saturated the day-to-day business life means they are better placed to determine how technology should be used to achieve the company’s objectives.
In the next five years, the economic climate will change, customer preferences will shift, and new competitors will threaten the business. Innovations in technology will provide new opportunities to explore, and new leadership could send the firm in a new direction. While most organizations have long-term growth targets, their strategies constantly evolve.
This new scenario has caused those in the enterprise architecture (EA) function to ask whether long-term road mapping is still a valuable investment.
EAs admit that long-term IT-led road mapping is no longer feasible. If the business does not have a detailed and stable five-year plan, these architects argue, how can IT develop a technology roadmap to help them achieve it? At best, creating long-term roadmaps is a waste of effort, a never-ending cycle of updates and revisions.
Without a long-range vision of business technology demand, IT has started to focus purely on the supply side. These architects focus on existing systems, identifying ways to reduce redundancies or improve flexibility. However, without a clear connection to business plans, they struggle to secure funding to make their plans a reality.
IT has turned their focus to the near-term, trying to influence the small decisions made every day in their organizations. IT can have greater impact, they believe, if they serve as advisors to IT and business stakeholders, guiding them to make cost-efficient, enterprise-aligned technology decisions.
Rather than taking a top-down perspective, shaping architecture through one master plan, they work from the bottom-up, encouraging more efficient working by influencing the myriad technology decisions being made each day.
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.
I have worked with several clients in the Internet of Things space over the last year and really enjoyed all of the engagements.
First, I am not a fan of the term IoT/Internet of Things. It just seems to be a bit too much pie in the sky and marketing. It reminds me a lot of the people who put an “e” or “i” in front of everything in the late 90s and early 00s. To me this is about expanded data integration use cases (e.g. more end points that you have the choice to access), data filtering and processing (e.g. what is the data you actually care about) and work flow/bpm from an enterprise perspective. (can you automate tasks and actions based on data or analysis of data)
There are definitely advancements in technology that are making for some very interesting solutions. My Nest thermostat is really cool, but it’s not really changing the world as one might think from some of the IoT frenzy the last few years. From what I have seen I think there are three main real world solutions that fit under the concept of IoT.
1) Passive Monitoring. This amounts to data collection and filtering. Lots of the consumer facing solutions fall into this category. Wearables, which we are told are super hot or just all the huge amount of big data collection that will then be churned and analyzed or just sit as it builds up. A big issue here is there is a lot of data to collect from an every growing set of end points but more data is not always useful if a company has not set up a process and a way to filter and identify the actual important data. I think the impact on individuals is more real than companies in this segment. I know people who swear they live better because of the data from their CPAP for example.
2) Active Monitoring. Most of these use cases fall into alerting or rule based work flow. There are examples of companies taking existing solutions or evolving existing solutions to then use real-time or near real-time data to drive work flow or alerts to make sure someone actually does something. My next write up is going to focus on an example in this space where a company has creating some really great technology to track usage of a product so they can provide real time view of inventory and then drive either automated replacement orders or work flow for people to do something like order more.
3) Automated response. To me a lot of the so called IoT use cases fall into a re-branding of solutions that have been around for years but now there is a mobile client. This is where all the security, energy (e.g. smart meters) and home automation fit.
Over the next 10 years I could see additional patterns become real, but a lot of the landscape is more hope than real when it comes to IoT from an enterprise company point of view or a how it really impacts a person’s life point of view. Of course I would expect other people would break down the use cases differently and I would love to hear your point of view.
(Note: IoT Landscape Chart is re-posted from work by First Mark Capital’s Matt Turck)
The verdict is in. Data is now broadly perceived as a source of competitive advantage. We all feel the heat to deliver good data. It is no wonder organizations view Analytics initiatives as highly strategic. But the big question is, can you really trust your data? Or are you just creating pretty visualizations on top of bad data?
We also know there is a shift towards self-service Analytics. But did you know that according to Gartner, “through 2016, less than 10% of self-service BI initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business”?1 This means that you may actually show up at your next big meeting and have data that contradicts your colleague’s data. Perhaps you are not working off of the same version of the truth. Maybe you have siloed data on different systems and they are not working in concert? Or is your definition of ‘revenue’ or ‘leads’ different from that of your colleague’s?
So are we taking our data for granted? Are we just assuming that it’s all available, clean, complete, integrated and consistent? As we work with organizations to support their Analytics journey, we often find that the harsh realities of data are quite different from perceptions. Let’s further investigate this perception gap.
For one, people may assume they can easily access all data. In reality, if data connectivity is not managed effectively, we often need to beg borrow and steal to get the right data from the right person. If we are lucky. In less fortunate scenarios, we may need to settle for partial data or a cheap substitute for the data we really wanted. And you know what they say, the only thing worse than no data is bad data. Right?
Another common misperception is: “Our data is clean. We have no data quality issues”. Wrong again. When we work with organizations to profile their data, they are often quite surprised to learn that their data is full of errors and gaps. One company recently discovered within one minute of starting their data profiling exercise, that millions of their customer records contained the company’s own address instead of the customers’ addresses… Oops.
Another myth is that all data is integrated. In reality, your data may reside in multiple locations: in the cloud, on premise, in Hadoop and on mainframe and anything in between. Integrating data from all these disparate and heterogeneous data sources is not a trivial task, unless you have the right tools.
And here is one more consideration to mull over. Do you find yourself manually hunting down and combining data to reproduce the same ad hoc report over and over again? Perhaps you often find yourself doing this in the wee hours of the night? Why reinvent the wheel? It would be more productive to automate the process of data ingestion and integration for reusable and shareable reports and Analytics.
Simply put, you need great data for great Analytics. We are excited to host Philip Russom of TDWI in a webinar to discuss how data management best practices can enable successful Analytics initiatives.
And how about you? Can you trust your data? Please join us for this webinar to learn more about building a trust-relationship with your data!
- Gartner Report, ‘Predicts 2015: Power Shift in Business Intelligence and Analytics Will Fuel Disruption’; Authors: Josh Parenteau, Neil Chandler, Rita L. Sallam, Douglas Laney, Alan D. Duncan; Nov 21 2014
A friend of mine recently reached out to me about some advice on CRM solutions in the market. Though I have not worked for a CRM vendor, I’ve had both direct experience working for companies that implemented such solutions to my current role interacting with large and small organizations regarding their data requirements to support ongoing application investments across industries. As we spoke, memories started to surface when he and I had worked on implementing Salesforce.com (SFDC) many years ago. Memories that we wanted to forget but important to call out given his new situation.
We worked together for a large mortgage lending software vendor selling loan origination solutions to brokers and small lenders mainly through email and snail mail based marketing. He was responsible for Marketing Operations, and I ran Product Marketing. The company looked at Salesforce.com to help streamline our sales operations and improve how we marketed and serviced our customers. The existing CRM system was from the early 90’s and though it did what the company needed it to do, it was heavily customized, costly to operate, and served its life. It was time to upgrade, to help grow the business, improve business productivity, and enhance customer relationships.
After 90 days of rolling out SFDC, we ran into some old familiar problems across the business. Sales reps continued to struggle in knowing who was a current customer using our software, marketing managers could not create quality mailing lists for prospecting purposes, and call center reps were not able to tell if the person on the other end was a customer or prospect. Everyone wondered why this was happening given we adopted the best CRM solution in the market. You can imagine the heartburn and ulcers we all had after making such a huge investment in our new CRM solution. C-Level executives were questioning our decisions and blaming the applications. The truth was, the issues were not related to SFDC but the data that we had migrated into the system and the lack proper governance and a capable information architecture to support the required data management integration between systems that caused these significant headaches.
During the implementation phase, IT imported our entire customer database of 200K+ unique customer entities from the old system to SFDC. Unfortunately, the mortgage industry was very transient and on average there were roughly 55K licenses mortgage brokers and lenders in the market and because no one ever validated the accuracy of who was really a customer vs. someone who had ever bought out product, we had a serious data quality issues including:
- Trial users who purchased evaluation copies of our products that expired were tagged as current customers
- Duplicate records caused by manual data entry errors consisting of companies with similar but entered slightly differently with the same business address were tagged as unique customers
- Subsidiaries of parent companies in different parts of the country that were tagged again as a unique customer.
- Lastly, we imported the marketing contact database of prospects which were incorrectly accounted for as a customer in the new system
We also failed to integrate real-time purchasing data and information from our procurement systems for sales and support to handle customer requests. Instead of integrating that data in real-time with proper technology, IT had manually loaded these records at the end of the week via FTP resulting in incorrect billing information, statement processing, and a ton of complaints from customers through our call center. The price we paid for not paying attention to our data quality and integration requirements before we rolled out Salesforce.com was significant for a company of our size. For example:
- Marketing got hit pretty hard. Each quarter we mailed evaluation copies of new products to our customer database of 200K, each costing the company $12 per to produce and mail. Total cost = $2.4M annually. Because we had such bad data, we would get 60% of our mailings returned because of invalid addresses or wrong contact information. The cost of bad data to marketing = $1.44M annually.
- Next, Sales struggled miserably when trying to upgrade a customer by running cold call campaigns using the names in the database. As a result, sales productivity dropped by 40% and experienced over 35% sales turnover that year. Within a year of using SFDC, our head of sales got let go. Not good!
- Customer support used SFDC to service customers, our average all times were 40 min per service ticket. We had believed that was “business as usual” until we surveyed what reps were spending their time each day and over 50% said it was dealing with billing issues caused by bad contact information in the CRM system.
At the end of our conversation, this was my advice to my friend:
- Conduct a data quality audit of the systems that would interact with the CRM system. Audit how complete your critical master and reference data is including names, addresses, customer ID, etc.
- Do this before you invest in a new CRM system. You may find that much of the challenges faced with your existing applications may be caused by the data gaps vs. the legacy application.
- If they had a data governance program, involve them in the CRM initiative to ensure they understand what your requirements are and see how they can help.
- However, if you do decide to modernize, collaborate and involve your IT teams, especially between your Application Development teams and your Enterprise Architects to ensure all of the best options are considered to handle your data sharing and migration needs.
- Lastly, consult with your technology partners including your new CRM vendor, they may be working with solution providers to help address these data issues as you are probably not the only one in this situation.
CRM systems have come a long way in today’s Big Data and Cloud Era. Many firms are adopting more flexible solutions offered through the Cloud like Salesforce.com, Microsoft Dynamics, and others. Regardless of how old or new, on premise or in the cloud, companies invest in CRM not to just serve their sales teams or increase marketing conversion rates, but to improve your business relationship with your customers. Period! It’s about ensuring you have data in these systems that is trustworthy, complete, up to date, and actionable to improve customer service and help drive sales of new products and services to increase wallet share. So how to do you maximize your business potential from these critical business applications?
Whether you are adopting your first CRM solution or upgrading an existing one, keep in mind that Customer Relationship Management is a business strategy, not just a software purchase. It’s also about having a sound and capable data management and governance strategy supported by people, processes, and technology to ensure you can:
- Access and migrate data from old to new avoiding develop cost overruns and project delays.
- Identify, detect, and distribute transactional and reference data from existing systems into your front line business application in real-time!
- Manage data quality errors including duplicate records, invalid names and contact information due to proper data governance and proactive data quality monitoring and measurement during and after deployment
- Govern and share authoritative master records of customer, contact, product, and other master data between systems in a trusted manner.
Will your data be ready for your new CRM investments? To learn more:
- Download Salesforce Integration for Dummies
- Download a new Whitepaper on how to Maximize Integration ROI with a Hybrid Approach
- Consolidating Multiple Salesforce Orgs: A Best Practice Guide
- Sign up for a 30 Day Trial of Informatica Cloud Integration
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