Tag Archives: Data Quality
Key findings from the report include:
- 65% of organizations cite data processing and integration as hampering distribution capability, with nearly half claiming their existing software and ERP is not suitable for distribution.
- Nearly two-thirds of enterprises have some form of distribution process, involving products or services.
- More than 80% of organizations have at least some problem with product or service distribution.
- More than 50% of CIOs in organizations with distribution processes believe better distribution would increase revenue and optimize business processes, with a further 38% citing reduced operating costs.
The core findings: “With better data integration comes better automation and decision making.”
This report is one of many I’ve seen over the years that come to the same conclusion. Most of those involved with the operations of the business don’t have access to key data points they need, thus they can’t automate tactical decisions, and also cannot “mine” the data, in terms of understanding the true state of the business.
The more businesses deal with building and moving products, the more data integration becomes an imperative value. As stated in this survey, as well as others, the large majority cite “data processing and integration as hampering distribution capabilities.”
Of course, these issues goes well beyond Australia. Most enterprises I’ve dealt with have some gap between the need to share key business data to support business processes, and decision support, and what current exists in terms of data integration capabilities.
The focus here is on the multiple values that data integration can bring. This includes:
- The ability to track everything as it moves from manufacturing, to inventory, to distribution, and beyond. You to bind these to core business processes, such as automatic reordering of parts to make more products, to fill inventory.
- The ability to see into the past, and to see into the future. The emerging approaches to predictive analytics allow businesses to finally see into the future. Also, to see what went truly right and truly wrong in the past.
While data integration technology has been around for decades, most businesses that both manufacture and distribute products have not taken full advantage of this technology. The reasons range from perceptions around affordability, to the skills required to maintain the data integration flow. However, the truth is that you really can’t afford to ignore data integration technology any longer. It’s time to create and deploy a data integration strategy, using the right technology.
This survey is just an instance of a pattern. Data integration was considered optional in the past. With today’s emerging notions around the strategic use of data, clearly, it’s no longer an option.
“Raw materials costs are the company’s single largest expense category,” said Steve Jenkins, Global IT Director at Valspar, at MDM Day in London. “Data management technology can help us improve business process efficiency, manage sourcing risk and reduce RFQ cycle times.”
Valspar is a $4 billion global manufacturing company, which produces a portfolio of leading paint and coating brands. At the end of 2013, the 200 year old company celebrated record sales and earnings. They also completed two acquisitions. Valspar now has 10,000 employees operating in 25 countries.
As is the case for many global companies, growth creates complexity. “Valspar has multiple business units with varying purchasing practices. We source raw materials from 1,000s of vendors around the globe,” shared Steve.
“We want to achieve economies of scale in purchasing to control spending,” Steve said as he shared Valspar’s improvement objectives. “We want to build stronger relationships with our preferred vendors. Also, we want to develop internal process efficiencies to realize additional savings.”
Poorly managed vendor and raw materials data was impacting Valspar’s buying power
The Valspar team, who sharply focuses on productivity, had an “Aha” moment. “We realized our buying power was limited by the age and quality of available vendor data and raw materials data,” revealed Steve.
The core vendor data and raw materials data that should have been the same across multiple systems wasn’t. Data was often missing or wrong. This made it difficult to calculate the total spend on raw materials. It was also hard to calculate the total cost of expedited freight of raw materials. So, employees used a manual, time-consuming and error-prone process to consolidate vendor data and raw materials data for reporting.
These data issues were getting in the way of achieving their improvement objectives. Valspar needed a data management solution.
Valspar needed a single trusted source of vendor and raw materials data
The team chose Informatica MDM, master data management (MDM) technology. It will be their enterprise hub for vendors and raw materials. It will manage this data centrally on an ongoing basis. With Informatica MDM, Valspar will have a single trusted source of vendor and raw materials data.
Informatica PowerCenter will access data from multiple source systems. Informatica Data Quality will profile the data before it goes into the hub. Then, after Informatica MDM does it’s magic, PowerCenter will deliver clean, consistent, connected and enriched data to target systems.
Better vendor and raw materials data management results in cost savings
Valspar expects to gain the following business benefits:
- Streamline the RFQ process to accelerate raw materials cost savings
- Reduce the total number of raw materials SKUs and vendors
- Increase productivity of staff focused on pulling and maintaining data
- Leverage consistent global data visibly to:
- increase leverage during contract negotiations
- improve acquisition due diligence reviews
- facilitate process standardization and reporting
Valspar’s vision is to tranform data and information into a trusted organizational assets
“Mastering vendor and raw materials data is Phase 1 of our vision to transform data and information into trusted organizational assets,” shared Steve. In Phase 2 the Valspar team will master customer data so they have immediate access to the total purchases of key global customers. In Phase 3, Valspar’s team will turn their attention to product or finished goods data.
Steve ended his presentation with some advice. “First, include your business counterparts in the process as early as possible. They need to own and drive the business case as well as the approval process. Also, master only the vendor and raw materials attributes required to realize the business benefit.”
Want more? Download the Total Supplier Information Management eBook. It covers:
- Why your fragmented supplier data is holding you back
- The cost of supplier data chaos
- The warning signs you need to be looking for
- How you can achieve Total Supplier Information Management
In my last blog I promised I would report back my experience on using Informatica Data Quality, a software tool that helps automate the hectic, tedious data plumbing task, a task that routinely consumes more than 80% of the analyst time. Today, I am happy to share what I’ve learned in the past couple of months.
But first, let me confess something. The reason it took me so long to get here was that I was dreaded by trying the software. Never a savvy computer programmer, I was convinced that I would not be technical enough to master the tool and it would turn into a lengthy learning experience. The mental barrier dragged me down for a couple of months and I finally bit the bullet and got my hands on the software. I am happy to report that my fear was truly unnecessary – It took me one half day to get a good handle on most features in the Analyst Tool, a component of the Data Quality designed for analyst and business users, then I spent 3 days trying to figure out how to maneuver the Developer Tool, another key piece of the Data Quality offering mostly used by – you guessed it, developers and technical users. I have to admit that I am no master of the Developer Tool after 3 days of wrestling with it, but, I got the basics and more importantly, my hands-on interaction with the entire software helped me understand the logic behind the overall design, and see for myself how analyst and business user can easily collaborate with their IT counterpart within our Data Quality environment.
To break it all down, first comes to Profiling. As analyst we understand too well the importance of profiling as it provides an anatomy of the raw data we collected. In many cases, it is a must have first step in data preparation (especially when our raw data came from different places and can also carry different formats). A heavy user of Excel, I used to rely on all the tricks available in the spreadsheet to gain visibility of my data. I would filter, sort, build pivot table, make charts to learn what’s in my raw data. Depending on how many columns in my data set, it could take hours, sometimes days just to figure out whether the data I received was any good at all, and how good it was.
Switching to the Analyst Tool in Data Quality, learning my raw data becomes a task of a few clicks – maximum 6 if I am picky about how I want it to be done. Basically I load my data, click on a couple of options, and let the software do the rest. A few seconds later I am able to visualize the statistics of the data fields I choose to examine, I can also measure the quality of the raw data by using Scorecard feature in the software. No more fiddling with spreadsheet and staring at busy rows and columns. Take a look at the above screenshots and let me know your preference?
Once I decide that my raw data is adequate enough to use after the profiling, I still need to clean up the nonsense in it before performing any analysis work, otherwise bad things can happen — we call it garbage in garbage out. Again, to clean and standardize my data, Excel came to rescue in the past. I would play with different functions and learn new ones, write macro or simply do it by hand. It was tedious but worked if I worked on static data set. Problem however, was when I needed to incorporate new data sources in a different format, many of the previously built formula would break loose and become inapplicable. I would have to start all over again. Spreadsheet tricks simply don’t scale in those situation.
With Data Quality Analyst Tool, I can use the Rule Builder to create a set of logical rules in hierarchical manner based on my objectives, and test those rules to see the immediate results. The nice thing is, those rules are not subject to data format, location, or size, so I can reuse them when the new data comes in. Profiling can be done at any time so I can re-examine my data after applying the rules, as many times as I like. Once I am satisfied with the rules, they will be passed on to my peers in IT so they can create executable rules based on the logic I create and run them automatically in production. No more worrying about the difference in format, volume or other discrepancies in the data sets, all the complexity is taken care of by the software, and all I need to do is to build meaningful rules to transform the data to the appropriate condition so I can have good quality data to work with for my analysis. Best part? I can do all of the above without hassling my IT – feeling empowered is awesome!
Use the right tool for the right job will improve our results, save us time, and make our jobs much more enjoyable. For me, no more Excel for data cleansing after trying our Data Quality software, because now I can get a more done in less time, and I am no longer stressed out by the lengthy process.
I encourage my analyst friends to try Informatica Data Quality, or at least the Analyst Tool in it. If you are like me, feeling weary about the steep learning curve then fear no more. Besides, if Data Quality can cut down your data cleansing time by half (mind you our customers have reported higher numbers), how many more predictive models you can build, how much you will learn, and how much faster you can build your reports in Tableau, with more confidence?
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.
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.
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.
Total Quality Management, as it relates to products and services has it’s roots in the 1920s. The 1960’s provided a huge boost with rise of guru’s such as Deming, Juran and Crosby. Whilst each had their own contribution, common principles for TQM that emerged in this era remain in practice today:
- Management (C-level management) is ultimately responsible for quality
- Poor quality has a cost
- The earlier in the process you address quality, the lower the cost of correcting it
- Quality should be designed into the system
So for 70 years industry in general has understood the cost of poor quality, and how to avoid these costs. So why is it that in 2014 I was party to a conversation that included the statement:
“I only came to the conference to see if you (Informatica) have solved the data quality problem.”
Ironically the TQM movement was only possible based on the analysis of data, but this is the one aspect that is widely ignored during TQM implementation. So much for ‘Total’ Quality Management.
This person is not alone in their thoughts. Many are waiting for the IT knight in shining armour, the latest and greatest data quality tools secured on their majestic steed, to ride in and save the day. Data quality dragon slayed, cold drinks all round, job done. This will not happen. Put data quality in the context of total quality management principles to see why: A single department cannot deliver data quality alone, regardless of the strength of their armoury.
I am not sure anyone would demand a guarantee of a high quality product from their machinery manufacturers. Communications providers cannot deliver high quality customer services organisations through technology alone. These suppliers have will have an influence on final product quality, but everyone understands equipment cannot deliver in isolation. Good quality raw materials, staff that genuinely takes pride in their work and the correct incentives are key to producing high quality products and services.
So why is there an expectation that data quality can be solved by tools alone?
At a minimum senior management support is required to push other departments to change their behaviour and/or values. So why aren’t senior management convinced that data quality is a problem worth their attention the way product & service quality is?
The fact that poor data quality has a high cost is reasonably well known via anecdotes. However, cost has not been well quantified, and hence fails to grab the attention of senior management. A 2005 paper by Richard Marsh[i] states: “Research and reports by industry experts, including Gartner Group, PriceWaterhouseCoopers and The Data Warehousing Institute clearly identify a crisis in data quality management and a reluctance among senior decision makers to do enough about it.” Little has changed since 2005.
However, we are living in a world where data generation, processing and consumption are increasing exponentially. With all the hype and investment in data, we face the grim prospect of fully embracing an age of data-driven-everything founded on a very poor quality raw material. Data quality is expected to be applied after generation, during the analytic phase. How much will that cost us? In order to function effectively, our new data-driven world must have high quality data running through every system and activity in an organization.
The Total Data Quality Movement is long overdue.
Only when every person in every organization understands the value of the data, do we have a chance of collectively solving the problem of poor data quality. Data quality must be considered from data generation, through transactional processing and analysis right until the point of archiving.
Informatica DQ supports IT departments in automating data correction where possible, and highlighting poor data for further attention where automation is not possible. MDM plays an important role in sustaining high quality data. Informatica tools empower the business to share the responsibility for total data quality.
We are ready for Total Data Quality, but continue to await the Total Data Quality Movement to get off the ground.
(If you do not have time to waiting for TDQM to gain traction, we can help you measure the cost of poor quality data in your organization to win corporate buy-in now.)
That second question is a killer because most people — no matter if they’re in marketing, sales or manufacturing — rely on incomplete, inaccurate or just plain wrong information. Regardless of industry, we’ve been fixated on historic transactions because that’s what our systems are designed to provide us.
“Moneyball: The Art of Winning an Unfair Game” gives a great example of what I mean. The book (not the movie) describes Billy Beane hiring MBAs to map out the factors that would win a baseball game. They discovered something completely unexpected: That getting more batters on base would tire out pitchers. It didn’t matter if batters had multi-base hits, and it didn’t even matter if they walked. What mattered was forcing pitchers to throw ball after ball as they faced an unrelenting string of batters. Beane stopped looking at RBIs, ERAs and even home runs, and started hiring batters who consistently reached first base. To me, the book illustrates that the most useful knowledge isn’t always what we’ve been programmed to depend on or what is delivered to us via one app or another.
For years, people across industries have turned to ERP, CRM and web analytics systems to forecast sales and acquire new customers. By their nature, such systems are transactional, forcing us to rely on history as the best predictor of the future. Sure it might be helpful for retailers to identify last year’s biggest customers, but that doesn’t tell them whose blogs, posts or Tweets influenced additional sales. Isn’t it time for all businesses, regardless of industry, to adopt a different point of view — one that we at Informatica call “Data-First”? Instead of relying solely on transactions, a data-first POV shines a light on interactions. It’s like having a high knowledge IQ about relationships and connections that matter.
A data-first POV changes everything. With it, companies can unleash the killer app, the killer sales organization and the killer marketing campaign. Imagine, for example, if a sales person meeting a new customer knew that person’s concerns, interests and business connections ahead of time? Couldn’t that knowledge — gleaned from Tweets, blogs, LinkedIn connections, online posts and transactional data — provide a window into the problems the prospect wants to solve?
That’s the premise of two startups I know about, and it illustrates how a data-first POV can fuel innovation for developers and their customers. Today, we’re awash in data-fueled things that are somehow attached to the Internet. Our cars, phones, thermostats and even our wristbands are generating and gleaning data in new and exciting ways. That’s knowledge begging to be put to good use. The winners will be the ones who figure out that knowledge truly is power, and wield that power to their advantage.
A mid-sized insurer recently approached our team for help. They wanted to understand how they fell short in making their case to their executives. Specifically, they proposed that fixing their customer data was key to supporting the executive team’s highly aggressive 3-year growth plan. (This plan was 3x today’s revenue). Given this core organizational mission – aside from being a warm and fuzzy place to work supporting its local community – the slam dunk solution to help here is simple. Just reducing the data migration effort around the next acquisition or avoiding the ritual annual, one-off data clean-up project already pays for any tool set enhancing data acquisitions, integration and hygiene. Will it get you to 3x today’s revenue? It probably won’t. What will help are the following:
Hard cost avoidance via software maintenance or consulting elimination is the easy part of the exercise. That is why CFOs love it and focus so much on it. It is easy to grasp and immediate (aka next quarter).
Soft cost reduction, like staff redundancies are a bit harder. Despite them being viable, in my experience very few decision makers want work on a business case to lay off staff. My team had one so far. They look at these savings as freed up capacity, which can be re-deployed more productively. Productivity is also a bit harder to quantify as you typically have to understand how data travels and gets worked on between departments.
However, revenue effects are even harder and esoteric to many people as they include projections. They are often considered “soft” benefits, although they outweigh the other areas by 2-3 times in terms of impact. Ultimately, every organization runs their strategy based on projections (see the insurer in my first paragraph).
The hardest to quantify is risk. Not only is it based on projections – often from a third party (Moody’s, TransUnion, etc.) – but few people understand it. More often, clients don’t even accept you investigating this area if you don’t have an advanced degree in insurance math. Nevertheless, risk can generate extra “soft” cost avoidance (beefing up reserve account balance creating opportunity cost) but also revenue (realizing a risk premium previously ignored). Often risk profiles change due to relationships, which can be links to new “horizontal” information (transactional attributes) or vertical (hierarchical) from parent-child relationships of an entity and the parent’s or children’s transactions.
Given the above, my initial advice to the insurer would be to look at the heartache of their last acquisition, use a benchmark for IT productivity from improved data management capabilities (typically 20-26% – Yankee Group) and there you go. This is just the IT side so consider increasing the upper range by 1.4x (Harvard Business School) as every attribute change (last mobile view date) requires additional meetings on a manager, director and VP level. These people’s time gets increasingly more expensive. You could also use Aberdeen’s benchmark of 13hrs per average master data attribute fix instead.
You can also look at productivity areas, which are typically overly measured. Let’s assume a call center rep spends 20% of the average call time of 12 minutes (depending on the call type – account or bill inquiry, dispute, etc.) understanding
- Who the customer is
- What he bought online and in-store
- If he tried to resolve his issue on the website or store
- How he uses equipment
- What he cares about
- If he prefers call backs, SMS or email confirmations
- His response rate to offers
- His/her value to the company
If he spends these 20% of every call stringing together insights from five applications and twelve screens instead of one frame in seconds, which is the same information in every application he touches, you just freed up 20% worth of his hourly compensation.
Then look at the software, hardware, maintenance and ongoing management of the likely customer record sources (pick the worst and best quality one based on your current understanding), which will end up in a centrally governed instance. Per DAMA, every duplicate record will cost you between $0.45 (party) and $0.85 (product) per transaction (edit touch). At the very least each record will be touched once a year (likely 3-5 times), so multiply your duplicated record count by that and you have your savings from just de-duplication. You can also use Aberdeen’s benchmark of 71 serious errors per 1,000 records, meaning the chance of transactional failure and required effort (% of one or more FTE’s daily workday) to fix is high. If this does not work for you, run a data profile with one of the many tools out there.
If standardization of records (zip codes, billing codes, currency, etc.) is the problem, ask your business partner how many customer contacts (calls, mailing, emails, orders, invoices or account statements) fail outright and/or require validation because of these attributes. Once again, if you apply the productivity gains mentioned earlier, there are you savings. If you look at the number of orders that get delayed in form of payment or revenue recognition and the average order amount by a week or a month, you were just able to quantify how much profit (multiply by operating margin) you would be able to pull into the current financial year from the next one.
The same is true for speeding up the introduction or a new product or a change to it generating profits earlier. Note that looking at the time value of funds realized earlier is too small in most instances especially in the current interest environment.
If emails bounce back or snail mail gets returned (no such address, no such name at this address, no such domain, no such user at this domain), e(mail) verification tools can help reduce the bounces. If every mail piece (forget email due to the miniscule cost) costs $1.25 – and this will vary by type of mailing (catalog, promotion post card, statement letter), incorrect or incomplete records are wasted cost. If you can, use fully loaded print cost incl. 3rd party data prep and returns handling. You will never capture all cost inputs but take a conservative stab.
If it was an offer, reduced bounces should also improve your response rate (also true for email now). Prospect mail response rates are typically around 1.2% (Direct Marketing Association), whereas phone response rates are around 8.2%. If you know that your current response rate is half that (for argument sake) and you send out 100,000 emails of which 1.3% (Silverpop) have customer data issues, then fixing 81-93% of them (our experience) will drop the bounce rate to under 0.3% meaning more emails will arrive/be relevant. This in turn multiplied by a standard conversion rate (MarketingSherpa) of 3% (industry and channel specific) and average order (your data) multiplied by operating margin gets you a benefit value for revenue.
If product data and inventory carrying cost or supplier spend are your issue, find out how many supplier shipments you receive every month, the average cost of a part (or cost range), apply the Aberdeen master data failure rate (71 in 1,000) to use cases around lack of or incorrect supersession or alternate part data, to assess the value of a single shipment’s overspend. You can also just use the ending inventory amount from the 10-k report and apply 3-10% improvement (Aberdeen) in a top-down approach. Alternatively, apply 3.2-4.9% to your annual supplier spend (KPMG).
You could also investigate the expediting or return cost of shipments in a period due to incorrectly aggregated customer forecasts, wrong or incomplete product information or wrong shipment instructions in a product or location profile. Apply Aberdeen’s 5% improvement rate and there you go.
Consider that a North American utility told us that just fixing their 200 Tier1 suppliers’ product information achieved an increase in discounts from $14 to $120 million. They also found that fixing one basic out of sixty attributes in one part category saves them over $200,000 annually.
So what ROI percentages would you find tolerable or justifiable for, say an EDW project, a CRM project, a new claims system, etc.? What would the annual savings or new revenue be that you were comfortable with? What was the craziest improvement you have seen coming to fruition, which nobody expected?
Next time, I will add some more “use cases” to the list and look at some philosophical implications of averages.
- It’s difficult to find and retain resource skills to staff big data projects
- It takes too long to deploy Big Data projects from ‘proof-of-concept’ to production
- Big data technologies are evolving too quickly to adapt
- Big Data projects fail to deliver the expected value
- It’s difficult to make Big Data fit-for-purpose, assess trust, and ensure security
Informatica has extended its leadership in data integration and data quality to Hadoop with our Big Data Edition to address all of these Big Data challenges.
The biggest challenge companies’ face is finding and retaining Big Data resource skills to staff their Big Data projects. One large global bank started their first Big Data project with 5 Java developers but as their Big Data initiative gained momentum they needed to hire 25 more Java developers that year. They quickly realized that while they had scaled their infrastructure to store and process massive volumes of data they could not scale the necessary resource skills to implement their Big Data projects. The research mentioned earlier indicates that 80% of the work in a Big Data project relates to data integration and data quality. With Informatica you can staff Big Data projects with readily available Informatica developers instead of an army of developers hand-coding in Java and other Hadoop programming languages. In addition, we’ve proven to our customers that Informatica developers are up to 5 times more productive on Hadoop than hand-coding and they don’t need to know how to program on Hadoop. A large Fortune 100 global manufacturer needed to hire 40 data scientists for their Big Data initiative. Do you really want these hard-to-find and expensive resources spending 80% of their time integrating and preparing data?
Another key challenge is that it takes too long to deploy Big Data projects to production. One of our Big Data Media and Entertainment customers told me prior to purchasing the Informatica Big Data Edition that most of his Big Data projects had failed. Naturally, I asked him why they had failed. His response was, “We have these hot-shot Java developers with a good idea which they prove out in our sandbox environment. But then when it comes time to deploy it to production they have to re-work a lot of code to make it perform and scale, make it highly available 24×7, have robust error-handling, and integrate with the rest of our production infrastructure. In addition, it is very difficult to maintain as things change. This results in project delays and cost overruns.” With Informatica, you can automate the entire data integration and data quality pipeline; everything you build in the development sandbox environment can be immediately and automatically deployed and scheduled for production as enterprise ready. Performance, scalability, and reliability are simply handled through configuration parameters without having to re-build or re-work any development which is typical with hand-coding. And Informatica makes it easier to reuse existing work and maintain Big Data projects as things change. The Big Data Editions is built on Vibe our virtual data machine and provides near universal connectivity so that you can quickly onboard new types of data of any volume and at any speed.
Big Data technologies are emerging and evolving extremely fast. This in turn becomes a barrier to innovation since these technologies evolve much too quickly for most organizations to adopt before the next big thing comes along. What if you place the wrong technology bet and find that it is obsolete before you barely get started? Hadoop is gaining tremendous adoption but it has evolved along with other big data technologies where there are literally hundreds of open source projects and commercial vendors in the Big Data landscape. Informatica is built on the Vibe virtual data machine which means that everything you built yesterday and build today can be deployed on the major big data technologies of tomorrow. Today it is five flavors of Hadoop but tomorrow it could be Hadoop and other technology platforms. One of our Big Data Edition customers, stated after purchasing the product that Informatica Big Data Edition with Vibe is our insurance policy to insulate our Big Data projects from changing technologies. In fact, existing Informatica customers can take PowerCenter mappings they built years ago, import them into the Big Data Edition and can run on Hadoop in many cases with minimal changes and effort.
Another complaint of business is that Big Data projects fail to deliver the expected value. In a recent survey (1), 86% Marketers say they could generate more revenue if they had a more complete picture of customers. We all know that the cost of us selling a product to an existing customer is only about 10 percent of selling the same product to a new customer. But, it’s not easy to cross-sell and up-sell to existing customers. Customer Relationship Management (CRM) initiatives help to address these challenges but they too often fail to deliver the expected business value. The impact is low marketing ROI, poor customer experience, customer churn, and missed sales opportunities. By using Informatica’s Big Data Edition with Master Data Management (MDM) to enrich customer master data with Big Data insights you can create a single, complete, view of customers that yields tremendous results. We call this real-time customer analytics and Informatica’s solution improves total customer experience by turning Big Data into actionable information so you can proactively engage with customers in real-time. For example, this solution enables customer service to know which customers are likely to churn in the next two weeks so they can take the next best action or in the case of sales and marketing determine next best offers based on customer online behavior to increase cross-sell and up-sell conversions.
Chief Data Officers and their analytics team find it difficult to make Big Data fit-for-purpose, assess trust, and ensure security. According to the business consulting firm Booz Allen Hamilton, “At some organizations, analysts may spend as much as 80 percent of their time preparing the data, leaving just 20 percent for conducting actual analysis” (2). This is not an efficient or effective way to use highly skilled and expensive data science and data management resource skills. They should be spending most of their time analyzing data and discovering valuable insights. The result of all this is project delays, cost overruns, and missed opportunities. The Informatica Intelligent Data platform supports a managed data lake as a single place to manage the supply and demand of data and converts raw big data into fit-for-purpose, trusted, and secure information. Think of this as a Big Data supply chain to collect, refine, govern, deliver, and manage your data assets so your analytics team can easily find, access, integrate and trust your data in a secure and automated fashion.
If you are embarking on a Big Data journey I encourage you to contact Informatica for a Big Data readiness assessment to ensure your success and avoid the pitfalls of the top 5 Big Data challenges.
- Gleanster Survey of 100 senior level marketers. The title of this survey is, Lifecycle Engagement: Imperatives for Midsize and Large Companies. Sponsored by YesMail.
- “The Data Lake: Take Big Data Beyond the Cloud”, Booz Allen Hamilton, 2013