Category Archives: Business Impact / Benefits

Fast and Fasterer: Screaming Streaming Data on Hadoop

Hadoop

Guest Post by Dale Kim

This is a guest blog post, written by Dale Kim, Director of Product Marketing at MapR Technologies.

Recent published research shows that “faster” is better than “slower.” The point, ladies and gentlemen, is that speed, for lack of a better word, is good. But granted, you won’t always have the need for speed. My Lamborghini is handy when I need to elude the Bakersfield fuzz on I-5, but it does nothing for my Costco trips. There, I go with capacity and haul home my 30-gallon tubs of ketchup with my Ford F150. (Note: this is a fictitious example, I don’t actually own an F150.)

But if speed is critical, like in your data streaming application, then Informatica Vibe Data Stream and the MapR Distribution including Apache™ Hadoop® are the technologies to use together. But since Vibe Data Stream works with any Hadoop distribution, my discussion here is more broadly applicable. I first discussed this topic earlier this year during my presentation at Informatica World 2014. In that talk, I also briefly described architectures that include streaming components, like the Lambda Architecture and enterprise data hubs. I recommend that any enterprise architect should become familiar with these high-level architectures.

Data streaming deals with a continuous flow of data, often at a fast rate. As you might’ve suspected by now, Vibe Data Stream, based on the Informatica Ultra Messaging technology, is great for that. With its roots in high speed trading in capital markets, Ultra Messaging quickly and reliably gets high value data from point A to point B. Vibe Data Stream adds management features to make it consumable by the rest of us, beyond stock trading. Not surprisingly, Vibe Data Stream can be used anywhere you need to quickly and reliably deliver data (just don’t use it for sharing your cat photos, please), and that’s what I discussed at Informatica World. Let me discuss two examples I gave.

Large Query Support. Let’s first look at “large queries.” I don’t mean the stuff you type on search engines, which are typically no more than 20 characters. I’m referring to an environment where the query is a huge block of data. For example, what if I have an image of an unidentified face, and I want to send it to a remote facial recognition service and immediately get the identity? The image would be the query, the facial recognition system could be run on Hadoop for fast divide-and-conquer processing, and the result would be the person’s name. There are many similar use cases that could leverage a high speed, reliable data delivery system along with a fast processing platform, to get immediate answers to a data-heavy question.

Data Warehouse Onload. For another example, we turn to our old friend the data warehouse. If you’ve been following all the industry talk about data warehouse optimization, you know pumping high speed data directly into your data warehouse is not an efficient use of your high value system. So instead, pipe your fast data streams into Hadoop, run some complex aggregations, then load that processed data into your warehouse. And you might consider freeing up large processing jobs from your data warehouse onto Hadoop. As you process and aggregate that data, you create a data flow cycle where you return enriched data back to the warehouse. This gives your end users efficient analysis on comprehensive data sets.

Hopefully this stirs up ideas on how you might deploy high speed streaming in your enterprise architecture. Expect to see many new stories of interesting streaming applications in the coming months and years, especially with the anticipated proliferation of internet-of-things and sensor data.

To learn more about Vibe Data Stream you can find it on the Informatica Marketplace .


 

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Ebola: Why Big Data Matters

Ebola: Why Big Data Matters

Ebola: Why Big Data Matters

The Ebola virus outbreak in West Africa has now claimed more than 4,000 lives and has entered the borders of the United States. While emergency response teams, hospitals, charities, and non-governmental organizations struggle to contain the virus, could big data analytics help?

A growing number of Data Scientists believe so.

If you recall the Cholera outbreak of Haiti in 2010 after the tragic earthquake, a joint research team from Karolinska Institute in Sweden and Columbia University in the US analyzed calling data from two million mobile phones on the Digicel Haiti network. This enabled the United Nations and other humanitarian agencies to understand population movements during the relief operations and during the subsequent cholera outbreak. They could allocate resources more efficiently and identify areas at increased risk of new cholera outbreaks.

Mobile phones, widely owned even in the poorest countries in Africa. Cell phones are also a rich source of data irrespective of which region where other reliable sources are sorely lacking. Senegal’s Orange Telecom provided Flowminder, a Swedish non-profit organization, with anonymized voice and text data from 150,000 mobile phones. Using this data, Flowminder drew up detailed maps of typical population movements in the region.

Today, authorities use this information to evaluate the best places to set up treatment centers, check-posts, and issue travel advisories in an attempt to contain the spread of the disease.

The first drawback is that this data is historic. Authorities really need to be able to map movements in real time especially since people’s movements tend to change during an epidemic.

The second drawback is, the scope of data provided by Orange Telecom is limited to a small region of West Africa.

Here is my recommendation to the Centers for Disease Control and Prevention (CDC):

  1. Increase the area for data collection to the entire region of Western Africa which covers over 2.1 million cell-phone subscribers.
  2. Collect mobile phone mast activity data to pinpoint where calls to helplines are mostly coming from, draw population heat maps, and population movement. A sharp increase in calls to a helpline is usually an early indicator of an outbreak.
  3. Overlay this data over censuses data to build up a richer picture.

The most positive impact we can have is to help emergency relief organizations and governments anticipate how a disease is likely to spread. Until now, they had to rely on anecdotal information, on-the-ground surveys, police, and hospital reports.

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Posted in B2B Data Exchange, Big Data, Business Impact / Benefits, Business/IT Collaboration, Data Governance, Data Integration | Tagged , , , , , , , | Leave a comment

Go On, Flip Your Division of Labor: More Time Analyzing and Less Time Prepping Data

Are you in Sales Operations, Marketing Operations, Sales Representative/Manager, or Marketing Professional? It’s no secret that if you are, you benefit greatly from the power of performing your own analysis, at your own rapid pace. When you have a hunch, you can easily test it out by visually analyzing data in Tableau without involving IT. When you are faced with tight timeframes in which to gain business insight from data, being able to do it yourself in the time you have available and without technical roadblocks makes all the difference.

Self-service Business Intelligence is powerful!  However, we all know it can be even more powerful. When needing to put together an analysis, we know that you spend about 80% of your time putting together data, and then just 20% of your time analyzing data to test out your hunch or gain your business insight. You don’t need to accept this anymore. We want you to know that there is a better way!

We want to allow you to Flip Your Division of Labor and allow you to spend more than 80% of your time analyzing data to test out your hunch or gain your business insight and less than 20% of your time putting together data for your Tableau analysis! That’s right. You like it. No, you love it. No, you are ready to run laps around your chair in sheer joy!! And you should feel this way. You now can spend more time on the higher value activity of gaining business insight from the data, and even find copious time to spend with your family. How’s that?

Project Springbok is a visionary new product designed by Informatica with the goal of making data access and data quality obstacles a thing of the past.  Springbok is meant for the Tableau user, a data person would rather spend their time visually exploring information and finding insight than struggling with complex calculations or waiting for IT. Project Springbok allows you to put together your data, rapidly, for subsequent analysis in Tableau. Project Springbok tells you things about your data that even you may not have known. It does it through Intelligent Suggestions that it presents to the User.

Let’s take a quick tour:

  • Project Springbok tells you, that you have a date column and that you likely want to obtain the Year and Quarter for your analysis (Fig 1)., And if you so wish, by a single click, voila, you have your corresponding years and even the quarters. And it all happened in mere seconds. A far cry from the 45 minutes it would have taken a fluent user of Excel to do using VLOOKUPS.

data

                                                                      Fig. 1

VALUE TO A MARKETING CAMPAIGN PROFESSIONAL: Rapidly validate and accurately complete your segmentation list, before you analyze your segments in Tableau. Base your segments on trusted data that did not take you days to validate and enrich.

  • Then Project Springbok will tell you that you have two datasets that could be joined on a common key, email for example, in each dataset, and would you like to move forward and join the datasets (Fig 2)? If you agree with Project Springbok’s suggestion, voila, dataset joined in a mere few seconds. Again, a far cry from the 45 minutes it would have taken a fluent user of Excel to do using VLOOKUPS.

Data

  Fig. 2

VALUE TO A SALES REPRESENTATIVE OR SALES MANAGER: You can now access your Salesforce.com data (Fig 3) and effortlessly combine it with ERP data to understand your true quota attainment. Never miss quota again due to a revenue split, be it territory or otherwise. Best of all, keep your attainment datatset refreshed and even know exactly what datapoint changed when your true attainment changes.

Data

Fig. 3

  • Then, if you want, Project Springbok will tell you that you have emails in the dataset, which you may or may not have known, but more importantly it will ask you if you wish to determine which emails can actually be mailed to. If you proceed, not only will Springbok check each email for correct structure (Fig 4), but will very soon determine if the email is indeed active, and one you can expect a response from. How long would that have taken you to do?

VALUE TO A TELESALES REPRESENTATIVE OR MARKETING EMAIL CAMPAIGN SPECIALIST : Ever thought you had a great email list and then found out most emails bounced? Now, confidently determine which emails are truly ones will be able to email to, before you send the message. Email prospects who you know are actually at the company and be confident you have their correct email addresses. You can then easily push the dataset into Tableau to analyze the trends in email list health.

Data

Fig. 4

 And, in case you were wondering, there is no training or install required for Project Springbok. The 80% of your time you used to spend on data preparation is now shrunk considerably, and this is after using only a few of Springbok’s capabilities. One more thing: You can even directly export from Project Springbok into Tableau via the “Export to Tableau TDE” menu item (Fig 5).  Project Springbok creates a Tableau TDE file and you just double click on it to open Tableau to test out your hunch or gain your business insight.

Data

Fig. 5

Here are some other things you should know, to convince you that you, too, can only spend no more than 20% of you time on putting together data for your subsequent Tableau analysis:

  • Springbok Sign-Up is Free
  • Springbok automatically finds problems with your data, and lets you fix them with a single click
  • Springbok suggests useful ways for you to combine different datasets, and lets you combine them effortlessly
  • Springbok suggests useful summarizations of your data, and lets you follow through on the summarizations with a single click
  • Springbok allows you to access data from your cloud or on-premise systems with a few clicks, and the automatically keep it refreshed. It will even tell you what data changed from the last time you saw it
  • Springbok allows you to collaborate by sharing your prepared data with others
  • Springbok easily exports your prepared data directly into Tableau for immediate analysis. You do not have to tell Tableau how to interpret the prepared data
  • Springbok requires no training or installation

Go on. Shift your division of labor in the right direction, fast. Sign-Up for Springbok and stop wasting precious time on data preparation. http://bit.ly/TabBlogs

———-

Are you going to be at Dreamforce this week in San Francisco?  Interested in seeing Project Springbok working with Tableau in a live demonstration?  Visit the Informatica or Tableau booths and see the power of these two solutions working hand-in-hand.Informatica is Booth #N1216 and Booth #9 in the Analytics Zone. Tableau is located in Booth N2112.

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The Case for Smart Data: When Big Data Isn’t Enough

Every two years, the typical company doubles the amount of data they store. However, this Data is inherently “dumb.” Acquiring more of it only seems to compound its lack of intellect.

When revitalizing your business, I won’t ask to look at your data – not even a little bit. Instead, we look at the process of how you use the data. What I want to know is this:

How much of your day-to-day operations are driven by your data?

The Case for Smart Data

I recently learned that 7-Eleven Japan has pushed decision-making down to the store level – in fact, to the level of clerks. Store clerks decide what goes on the shelves in their individual 7-Eleven stores. These clerks push incredible inventory turns. Some 70% of the products on the shelves are new to stores each year. As a result, this chain has been the most profitable Japanese retailer for 30 years running.

Click to enlarge

Click to enlarge

Instead of just reading the data and making wild guesses on why something works and why something doesn’t, these clerks acquired the skill of looking at the quantitative and the qualitative and connected dots. Data told them what people are talking about, how it’s related to their product and how much weight it carried. You can achieve this as well. To do so, you must introduce a culture that emphasizes discipline around processes. A disciplined process culture uses:

  1. A template approach to data with common processes, reuse of components, and a single face presented to customers
  2. Employees who consistently follow standard procedures

If you cannot develop such company-wide consistency, you will not gain benefits of ERP or CRM systems.

Make data available to the masses. Like at 7-Eleven Japan, don’t centralize the data decision-making process. Instead, push it out to the ranks. By putting these cultures and practices into play, businesses can use data to run smarter.

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Posted in Big Data, Business Impact / Benefits, Business/IT Collaboration, Data Synchronization, Data Transformation, Data Warehousing, Master Data Management, Retail | Tagged , , , , , | Leave a comment

CFO Move to Chief Profitability Officer

30% or higher of each company’s businesses are unprofitable

cfoAccording to Jonathan Brynes at the MIT Sloan School, “the most important issue facing most managers …is making more money from their existing businesses without costly new initiatives”. In Brynes’ cross industry research, he found that 30% or higher of each company’s businesses are unprofitable. Brynes claims these business losses are offset by what are “islands of high profitability”. The root cause of this issue is asserted to be the inability of current financial and management control systems to surface profitability problems and opportunities. Why is this the case? Byrnes believes that management budgetary guidance by its very nature assumes the continuation of the status quo. For this reason, the response to management asking for a revenue increase is to increase revenues for businesses that are profitable and unprofitable. Given this, “the areas of embedded unprofitability remain embedded and largely invisible”. At the same time to be completely fair, it should be recognized that it takes significant labor to accurately and completely put together a complete picture on direct and indirect costs.

The CFO needs to become the point person on profitability issues

cfo

Byrnes believes, nevertheless, that CFOs need to become the corporate point person for surfacing profitability issues. They, in fact, should act as the leader of a new and important role, the chief profitability officer. This may seem like an odd suggestion since virtually every CFO if asked would view profitability as a core element of their job. But Byrnes believes that CFOs need to move beyond broad, departmental performance measures and build profitability management processes into their companies’ core management activities. This task requires the CFO to determine two things.

  1. Which product lines, customers, segments, and channels are unprofitable so investments can be reduced or even eliminated?
  2. Which product lines, customers, segments, and channels are the most profitable so management can determine whether to expand investments and supporting operations?

Why didn’t portfolio management solve this problem?

cfoNow as a strategy MBA, Byrnes’ suggestion leave me wondering why the analysis proposed by strategy consultants like Boston Consulting Group didn’t solve this problem a long time ago. After all portfolio analysis has at its core the notion that relative market share and growth rate will determine profitability and which businesses a firm should build share, hold share, harvest share, or divest share—i.e. reduce, eliminate, or expand investment. The truth is getting at these figures, especially profitability, is a time consuming effort.

KPMG finds 91% of CFOs are held back by financial and performance systems

KPMG

As financial and business systems have become more complex, it has become harder and harder to holistically analyze customer and product profitability because the relevant data is spread over a myriad of systems, technologies, and locations. For this reason, 91% of CFO respondents in a recent KPMG survey said that they want to improve the quality of their financial and performance insight from the data they produce. An amazing 51% of these CFOs, also, admitted that the “collection, storage, and retrieval financial and performance data at their company is primarily a manual and/or spreadsheet-based exercise”. Think about it — a majority of these CFOs teams time is spent collecting financial data rather than actively managing corporate profitability.

How do we fix things?

FixWhat is needed is a solution that allows financial teams to proactively produce trustworthy financial data from each and every financial system and then reliably combine and aggregate the data coming from multiple financial systems. Having accomplished this, the solution needs to allow financial organizations to slice and dice net profitability for product lines and customers.

This approach would not only allow financial organizations to cut their financial operational costs but more importantly drive better business profitability by surfacing profitability gaps. At the same time, it would enable financial organizations to assist business units in making more informed customer and product line investment decisions. If a product line or business is narrowly profitable and lacks a broader strategic context or ability to increase profitability by growing market share, it is a candidate for investment reduction or elimination.

Strategic CFOs need to start asking questions of their business counterparts starting with their justification for their investment strategy. Key to doing this involves consolidating reliable profitability data across customers, products, channel partners, suppliers. This would eliminate the time spent searching for and manually reconciling data in different formats across multiple systems. It should deliver ready analysis across locations, applications, channels, and departments.

Some parting thoughts

Strategic CFOs tell us they are trying to seize the opportunity “to be a business person versus a bean counting historically oriented CPA”. I believe a key element of this is seizing the opportunity to become the firm’s chief profitability officer. To do this well, CFOs need dependable data that can be sliced and diced by business dimensions. Armed with this information, CFOs can determine the most and least profitability, businesses, product lines, and customers. As well, they can come to the business table with the perspective to help guide their company’s success.

Related links
Solution Brief: The Intelligent Data Platform
Related Blogs
CFOs Discuss Their Technology Priorities
The CFO Viewpoint upon Data
How CFOs can change the conversation with their CIO?
New type of CFO represents a potent CIO ally
Competing on Analytics
The Business Case for Better Data Connectivity

Twitter: @MylesSuer

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Building a Data Foundation for Execution

Building a Data Foundation for Execution

Building a Data Foundation

I have been re-reading Enterprise Architecture as Strategy from the MIT Center for Information Systems Research (CISR).*  One concept that they talk about that jumped out at me was the idea of a “Foundation for Execution.”  Everybody is working to drive new business initiatives, to digitize their businesses, and to thrive in an era of increased technology disruption and competition.  The ideas around a Foundation for Execution in the book are a highly practical and useful framework to deal with these problems.

This got me thinking: What is the biggest bottleneck in the delivery of business value today?  I know I look at things from a data perspective, but data is the biggest bottleneck.  Consider this prediction from Gartner:

“Gartner predicts organizations will spend one-third more on app integration in 2016 than they did in 2013. What’s more, by 2018, more than half the cost of implementing new large systems will be spent on integration. “

When we talk about application integration, we’re talking about moving data, synchronizing data, cleansing, data, transforming data, testing data.  The question for architects and senior management is this: Do you have the Data Foundation for Execution you need to drive the business results you require to compete?  The answer, unfortunately, for most companies is; No.

All too often data management is an add-on to larger application-based projects.  The result is unconnected and non-interoperable islands of data across the organization.  That simply is not going to work in the coming competitive environment.  Here are a couple of quick examples:

  • Many companies are looking to compete on their use of analytics.  That requires collecting, managing, and analyzing data from multiple internal and external sources.
  • Many companies are focusing on a better customer experience to drive their business. This again requires data from many internal sources, plus social, mobile and location-based data to be effective.

When I talk to architects about the business risks of not having a shared data architecture, and common tools and practices for enterprise data management, they “get” the problem.  So why aren’t they addressing it?  The issue is that they find that they are only funded to do the project they are working on and are dealing with very demanding timeframe requirements.  They have no funding or mandate to solve the larger enterprise data management problem, which is getting more complex and brittle with each new un-connected project or initiative that is added to the pile.

Studies such as “The Data Directive” by The Economist show that organizations that actively manage their data are more successful. But, if that is the desired future state, how do you get there?

Changing an organization to look at data as the fuel that drives strategy takes hard work and leadership. It also takes a strong enterprise data architecture vision and strategy.  For fresh thinking on the subject of building a data foundation for execution, see “Think Data-First to Drive Business Value” from Informatica.

* By the way, Informatica is proud to announce that we are now a sponsor of the MIT Center for Information Systems Research.

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Posted in Architects, Business Impact / Benefits, CIO, Data Governance, Data Integration, Data Synchronization, Enterprise Data Management | Tagged , , , , , , | Leave a comment

CSI: “Enter Location Here”

Last time I talked about how benchmark data can be used in IT and business use cases to illustrate the financial value of data management technologies.  This time, let’s look at additional use cases, and at how to philosophically interpret the findings.

ROI interpretation

We have all philosophies covered

So here are some additional areas of investigation for justifying a data quality based data management initiative:

  • Compliance or any audits data and report preparation and rebuttal  (FTE cost as above)
  • Excess insurance premiums on incorrect asset or party information
  • Excess tax payments due to incorrect asset configuration or location
  • Excess travel or idle time between jobs due to incorrect location information
  • Excess equipment downtime (not revenue generating) or MTTR due to incorrect asset profile or misaligned reference data not triggering timely repairs
  • Equipment location or ownership data incorrect splitting service cost or revenues incorrectly
  • Party relationship data not tied together creating duplicate contacts or less relevant offers and lower response rates
  • Lower than industry average cross-sell conversion ratio due to inability to match and link departmental customer records and underlying transactions and expose them to all POS channels
  • Lower than industry average customer retention rate due to lack of full client transactional profile across channels or product lines to improve service experience or apply discounts
  • Low annual supplier discounts due to incorrect or missing alternate product data or aggregated channel purchase data

I could go on forever, but allow me to touch on a sensitive topic – fines. Fines, or performance penalties by private or government entities, only make sense to bake into your analysis if they happen repeatedly in fairly predictable intervals and are “relatively” small per incidence.  They should be treated like M&A activity. Nobody will buy into cost savings in the gazillions if a transaction only happens once every ten years. That’s like building a business case for a lottery win or a life insurance payout with a sample size of a family.  Sure, if it happens you just made the case but will it happen…soon?

Use benchmarks and ranges wisely but don’t over-think the exercise either.  It will become paralysis by analysis.  If you want to make it super-scientific, hire an expensive consulting firm for a 3 month $250,000 to $500,000 engagement and have every staffer spend a few days with them away from their day job to make you feel 10% better about the numbers.  Was that worth half a million dollars just in 3rd party cost?  You be the judge.

In the end, you are trying to find out and position if a technology will fix a $50,000, $5 million or $50 million problem.  You are also trying to gauge where key areas of improvement are in terms of value and correlate the associated cost (higher value normally equals higher cost due to higher complexity) and risk.  After all, who wants to stand before a budget committee, prophesy massive savings in one area and then fail because it would have been smarter to start with something simpler and quicker win to build upon?

The secret sauce to avoiding this consulting expense and risk is a natural curiosity, willingness to do the legwork of finding industry benchmark data, knowing what goes into them (process versus data improvement capabilities) to avoid inappropriate extrapolation and using sensitivity analysis to hedge your bets.  Moreover, trust an (internal?) expert to indicate wider implications and trade-offs.  Most importantly, you have to be a communicator willing to talk to many folks on the business side and have criminal interrogation qualities, not unlike in your run-of-the-mill crime show.  Some folks just don’t want to talk, often because they have ulterior motives (protecting their legacy investment or process) or hiding skeletons in the closet (recent bad performance).  In this case, find more amenable people to quiz or pry the information out of these tough nuts, if you can.

CSI: "Enter Location Here"

CSI: “Enter Location Here”

Lastly; if you find ROI numbers, which appear astronomical at first, remember that leverage is a key factor.  If a technical capability touches one application (credit risk scoring engine), one process (quotation), one type of transaction (talent management self-service), a limited set of people (procurement), the ROI will be lower than a technology touching multiple of each of the aforementioned.  If your business model drives thousands of high-value (thousands of dollars) transactions versus ten twenty-million dollar ones or twenty-million one-dollar ones, your ROI will be higher.  After all, consider this; retail e-mail marketing campaigns average an ROI of 578% (softwareprojects.com) and this with really bad data.   Imagine what improved data can do just on that front.

I found massive differences between what improved asset data can deliver in a petrochemical or utility company versus product data in a fashion retailer or customer (loyalty) data in a hospitality chain.   The assertion of cum hoc ergo propter hoc is a key assumption how technology delivers financial value.  As long as the business folks agree or can fence in the relationship, you are on the right path.

What’s your best and worst job to justify someone giving you money to invest?  Share that story.

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Informatica’s Inclusion on the “R&D All-Stars: CNBC RQ 50″ Was No Accident

CNBC RQ 50Earlier this month, CNBC.com published its first ever R&D All-Stars: CNBC RQ 50, ranking the top 50 public companies by return on research and development investment. Coming in the top ten, and the first pure software play was Informatica, mentioned as first among great software companies like Google, Amazon, and Salesforce. CNBC.com is referencing a companion article by David Spiegel – Boring stocks that generate R&D heat-and profits. The article made an excellent point: When R&D productivity links R&D spending to corporate revenue growth and market value, it is a better gauge of the productivity of that spending.

Unlike other R&D lists or rankings, the RQ50 was less concerned with pure dollars than what the company actually did with it. The RQ50 measures increase in revenue as it relates to increase in R&D expenditures. Its methodology was provided by Professor Anne Marie Knott, of Washington University in St. Louis, who tracks and studies corporate R&D investment, and has found that the companies that regularly turn R&D into income typically place innovation at the forefront of the corporate mission and have a structure and culture that support it.

Informatica is on the list because its revenue gains between 2006 and 2013 correlate directly with its increased R&D investment over the same period. While the list specifically cites the 2013 figures, the result is due to a systematic and long-term strategic initiative to place innovation at the core of our business plan.

Informatica has innovated broadly across its product spectrum. I can personally speak to one area where it has invested smartly and made significant gains – Informatica Cloud. Informatica decided to make its initial investment in the cloud in 2006 and was early in the market with regards to cloud integration. In fact, back in 2006, very few of today’s well-known SaaS companies were even publicly traded. The most popular SaaS app today, Salesforce.com had revenues of just $309 million in FY2006 compared with over $4 billion in FY2014. Amazon EC2, one of the core services of Amazon Web Services (AWS) itself had only been announced in that year. Apart from EC2, Amazon only had six other services in 2006. In 2014, that number has ballooned to over 30.

In his article about the RQ50, Spiegel talks about how the companies on the list aren’t just listening to what customers want or need now. They’re also challenging themselves to come up with the things the market can use two or ten years into the future. In 2006, Informatica took the same approach with its initial investment in cloud integration.

For us, it started with an observation and then a commitment to the belief that we were at an inflection point with the cloud, and on the cusp of what was going to become a true megatrend that represented a huge opportunity for the integration industry. Informatica assembled a small, agile group made up of strong leaders with varying skills and experience pulled from different areas—sales, engineering, and product management — throughout the company. It also meant throwing away the traditional measures of success and identifying new and more appropriate metrics to benchmark our progress. And finally, it included partnering with like-minded companies like Salesforce and NetSuite initially, and later on with Amazon, and taking our core strength – on-premise data integration technology – and pivoting it into a new direction.

The result was the first iteration of the Informatica Cloud. It leveraged the fruit of our R&D investment – the Vibe Virtual Data Machine – to provide SaaS administrators and line of business IT with the ability to perform lightweight cloud integrations between their on-premise and cloud applications without the involvement of an integration developer. Subsequent work and innovation have continued along the same path, adding tools like drag-and-drop design interfaces and mapping wizards, with the end goal of giving line-of-business (LOB) IT, cloud application administrators and citizen integrators a single platform to perform all the integration patterns they require, on their timeline. Informatica Cloud has consistently delivered 2-3 releases every year, and is now already on Release 20. From originally starting out with Data Replication for Salesforce, the Cloud team added bigger and better functionality such as developing connectivity for over 100 applications and data protocols, opening up our integration services through REST APIs, going beyond integration by incorporating cloud master data management and cloud test data management capabilities, and most recently announcing optimized batch and real-time cloud integration under a single unified platform.

And it goes on to this day, with investments in new innovations and directions, like Informatica Project Springbok. With Project Springbok, we’re duplicating what we did with Informatica Cloud but this time for citizen integrators. We’re using our vast experiences working with customers and building cutting-edge technology IP over the last 20 years and enabling citizen integrators to harmonize data faster for better insights (and hopefully, less late nights writing spreadsheet formulas). What we do after Project Springbok is anyone’s guess, but wherever that is, it will be sure to put us on lists like the RQ 50 for some time to come.

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Is the Internet of Things relevant for the government?

Get connected. Be connected. Make connections. Find connections. The Internet of Things (IoT) is all about connecting people, processes, data and, as the name suggests, things. The recent social media frenzy surrounding the ALS Ice Bucket Challenge has certainly reminded everyone of the power of social media, the Internet and a willingness to answer a challenge. Fueled by personal and professional connections, the craze has transformed fund raising for at least one charity. Similarly, IoT may potentially be transformational to the business of the public sector, should government step up to the challenge.

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Is the Internet of Things relevant for the government?

Government is struggling with the concept and reality of how IoT really relates to the business of government, and perhaps rightfully so. For commercial enterprises, IoT is far more tangible and simply more fun. Gaming, televisions, watches, Google glasses, smartphones and tablets are all about delivering over-the-top, new and exciting consumer experiences. Industry is delivering transformational innovations, which are connecting people to places, data and other people at a record pace.

It’s time to accept the challenge. Government agencies need to keep pace with their commercial counterparts and harness the power of the Internet of Things. The end game is not to deliver new, faster, smaller, cooler electronics; the end game is to create solutions that let devices connecting to the Internet interact and share data, regardless of their location, manufacturer or format and make or find connections that may have been previously undetectable. For some, this concept is as foreign or scary as pouring ice water over their heads. For others, the new opportunity to transform policy, service delivery, leadership, legislation and regulation is fueling a transformation in government. And it starts with one connection.

One way to start could be linking previously siloed systems together or creating a golden record of all citizen interactions through a Master Data Management (MDM) initiative. It could start with a big data and analytics project to determine and mitigate risk factors in education or linking sensor data across multiple networks to increase intelligence about potential hacking or breaches. Agencies could stop waste, fraud and abuse before it happens by linking critical payment, procurement and geospatial data together in real time.

This is the Internet of Things for government. This is the challenge. This is transformation.

This article was originally published on www.federaltimes.com. Please view the original listing here

 

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Posted in Big Data, Business Impact / Benefits, Data Integration, Data Security, Master Data Management, Public Sector, Uncategorized | Tagged , , , , , | Leave a comment