Category Archives: Application Retirement
When’s the last time you visited your local branch bank and spoke to a human being? How about talking to your banker over the phone? Can’t remember? Well you’re not alone and don’t worry, it’s not a bad thing. The days of operating physical branches with expensive workers to greet and service customers are being replaced with more modern and customer friendly mobile banking applications that allow consumers to deposit checks from the phone, apply for a mortgage and sign closing documents electronically, to eliminating the need to go to an ATM and get physical cash by using mobile payment solutions like Apple Pay. In fact, a new report titled ‘Bricks + Clicks: Building the Digital Branch,’ from Jeanne Capachin and Jim Marous takes an in-depth look at how banks and credit unions are changing their branch and customer channel strategies to meet the demand of today’s digital banking customer.
Why am I talking about this? These market trends are dominating the CEO and CIO agenda in today’s banking industry. I just returned from the 2015 IDC Asian Financial Congress event in Singapore where the digital journey for the next generation bank was a major agenda item. According the IDC Financial Insights, global banks will invest $31.5B USD in core banking modernization to enable these services, improve operational efficiency, and position these banks to better compete on technology and convenience across markets. Core banking modernization initiatives are complex, costly, and fraught with risks. Let’s take a closer look. (more…)
In our house when we paint a room, my husband does the big rolling of the walls or ceiling, I do the cut-in work. I am good at prepping the room, taping all the trim and deliberately painting the corners. However, I am thrifty and constantly concerned that we won’t have enough paint to finish a room. My husband isn’t afraid to use enough paint and is extremely efficient at painting a wall in a single even coat. As a result, I don’t do the big rolling and he doesn’t do the cutting in. It took us awhile to figure this out, and a few rooms had to be repainted while we were figuring it out. Now we know what we are good at, and what we need help with.
Payers roles are changing. Payers were previously focused on risk assessment, setting and collecting premiums, analyzing claims and making payments – all while optimizing revenues. Payers are pretty good at selling to employers, figuring out the cost/benefit ratio from an employers perspective and ensuring a good, profitable product. With the advent of the Affordable Healthcare Act along with a much more transient insured population, payers now must focus more on the individual insured and be able to communicate with the individuals in a more nimble manner than in the past.
Individual members will shop for insurance based on consumer feedback and price. They are interested in ease of enrollment and the ability to submit and substantiate claims quickly and intuitively. Payers are discovering that they need to help manage population health at a individual member level. And population health management requires less of a business-data analytics approach and more social media and gaming-style logic to understand patients. In this way, payers can help develop interventions to sustain behavioral changes for better health.
When designing such analytics, payers should consider the following key design steps:
- Extend data warehouses to an analytics appliance
- Invest in a big data platform to absorb patients’ social data
- Build predictive analytics for patient behavior
- Bridge collaborative and behavioral analytics with claims to build revenue and profitability
Due to payers’ mature predictive analytics competencies, they will have a much easier time in the next generation of population behavior compared to their provider counterparts. As clinical content is often unstructured compared to the claims data, payers need to pay extra attention to context and semantics when deciphering clinical content submitted by providers. Payers can use help from vendors that can help them understand unstructured data, individual members. They can then use that data to create fantastic predictive analytic solutions.
Every year, I get a replacement desk calendar to help keep all of our activities straight – and for a family of four, that is no easy task. I start with taking all of the little appointment cards the dentist, orthodontist, pediatrician and GP give to us for appointments that occur beyond the current calendar dates. I transcribe them all. Then I go through last year’s calendar to transfer any information that is relevant to this year’s calendar. And finally, I put the calendar down in the basement next to previous year calendars so I can refer back to them if I need. Last year’s calendar contains a lot of useful information, but no longer has the ability to solve my need to organize schedules for this year.
In a very loose way – this is very similar to application retirement. Many larger health plans have existing systems that were created several years (sometimes even several decades) ago. These legacy systems have been customized to reflect the health plan’s very specific business processes. They may be hosted on costly hardware, developed in antiquated software languages and rely on a few developers that are very close to retirement. The cost of supporting these (most likely) antiquated systems can be diverting valuable dollars away from innovation.
The process that I use to move appointment and contact data from one calendar to the next works for me – but is relatively small in scale. Imagine if I was trying to do this for an entire organization without losing context, detail or accuracy!
There are several methodologies for determining the best strategy for your organization to approach software modernization, including:
- Architecture Driven Modernization (ADM) is the initiative to standardize views of the existing systems in order to enable common modernization activities like code analysis and comprehension, and software transformation.
- SABA (Bennett et al., 1999) is a high-level framework for planning the evolution and migration of legacy systems, taking into account both organizational and technical issues.
- SRRT (Economic Model to Software Rewriting and Replacement Times), Chan et al. (1996), Formal model for determining optimal software rewrite and replacement timings based on versatile metrics data.
- And if all else fails: Model Driven Engineering (MDE) is being investigated as an approach for reverse engineering and then forward engineering software code
My calendar migration process evolved over time, your method for software modernization should be well planned prior to the go-live date for the new software system.
Have you noticed something different this winter season that most people are cheery about? I’ll give you a hint. It’s not the great sales going on at your local shopping mall but something that helps you get to the mall allot more affordable then last year. It’s the extremely low gas prices across the globe, fueled by over-supply of oil vs. demand contributed from a boom in Geo-politics and boom in shale oil production in N. America and abroad. Like any other commodity, it’s impossible to predict where oil prices are headed however, one thing is sure that Oil and Gas companies will need timely and quality data as firms are investing in new technologies to become more agile, innovative, efficient, and competitive as reported by a recent IDC Energy Insights Predictions report for 2015.
The report predicts:
- 80% of the top O&G companies will reengineer processes and systems to optimize logistics, hedge risk and efficiently and safely deliver crude, LNG, and refined products by the end of 2017.
- Over the next 3 years, 40% of O&G majors and all software divisions of oilfield services (OFS) will co-innovate on domain specific technical projects with IT professional service firms.
- The CEO will expect immediate and accurate information about top Shale Plays to be available by the end of 2015 to improve asset value by 30%.
- By 2016, 70% percent of O&G companies will have invested in programs to evolve the IT environment to a third platform driven architecture to support agility and readily adapt to change.
- With continued labor shortages and over 1/3 of the O&G workforce under 45 in three years, O&G companies will turn to IT to meet productivity goals.
- By the end of 2017, 100% of the top 25 O&G companies will apply modeling and simulation tools and services to optimize oil field development programs and 25% will require these tools.
- Spending on connectivity related technologies will increase by 30% between 2014 and 2016, as O&G companies demand vendors provide the right balance of connectivity for a more complex set of data sources.
- In 2015, mergers, acquisitions and divestitures, plus new integrated capabilities, will drive 40% of O&G companies to re-evaluate their current deployments of ERP and hydrocarbon accounting.
- With a business case built on predictive analytics and optimization in drilling, production and asset integrity, 50% of O&G companies will have advanced analytics capabilities in place by 2016.
- With pressures on capital efficiency, by 2015, 25% of the Top 25 O&G companies will apply integrated planning and information to large capital projects, speeding up delivery and reducing over-budget risks by 30%.
Realizing value from these investments will also require Oil and Gas firms to modernize and improve their data management infrastructure and technologies to deliver great data whether to fuel actionable insights from Big Data technology to facilitating post-merger application consolidation and integration activities. Great data is only achievable by Great Design supported by capable solutions designed to help access and deliver timely, trusted, and secure data to need it most.
Lack of proper data management investments and competences have long plagued the oil and gas sector with “less-than acceptable” data and higher operating costs. According to the “Upstream Data and Information Management Survey” conducted by Wipro Technologies, 56% of those surveyed felt that business users spent more than ¼ or more of their time on low value activities caused by existing data issues (e.g. accessing, cleansing, preparing data) for “high value” activities (e.g. analysis, planning, decision making). The same survey showed the biggest data management issues were timely access to required data and data quality issues from source systems.
So what can Oil and Gas CIO’s and Enterprise Architects do to prepare for the future? Here are some tips for consideration:
- Look to migrate and automate legacy hand coded data transformation processes by adopting tools that can help streamline the development, testing, deployment, and maintenance of these complex tasks that help developers build, maintain, and monitor data transformation rules once and deploy them across the enterprise.
- Simplify how data is distributed across systems with more modern architectures and solutions and avoid the cost and complexities of point to point integrations
- Deal with and manage data quality upstream at the source and throughout the data life cycle vs. having end users fix unforeseen data quality errors manually.
- Create a centralized source of shared business reference and master data that can manage a consistent record across heterogeneous systems such as well asset/material information (wellhead, field, pump, valve, etc.), employee data (drill/reservoir engineer, technician), location data (often geo-spatial), and accounting data (for financial roll-ups of cost, production data).
- Establish standards and repeatable best practices by adopting an Integration Competency Center frame work to support the integration and sharing of data between operational and analytical systems.
In summary, low oil prices have a direct and positive impact to consumers especially during the winter season and holidays and I personally hope they continue for the unforeseeable future given that prices were double just a year ago. Unfortunately, no one can predict future energy prices however one thing is for sure, the demand for great data by Oil and Gas companies will continue to grow. As such, CIO’s and Enterprise Architects will need to consider and recognize the importance of improving their data management capabilities and technologies to ensure success in 2015. How ready are you?
Click to learn more about Informatica in today’s Energy Sector:
The title of this article may seem counterintuitive, but the reality is that the business doesn’t care about data. They care about their business processes and outcomes that generate real value for the organization. All IT professionals know there is huge value in quality data and in having it integrated and consistent across the enterprise. The challenge is how to prove the business value of data if the business doesn’t care about it. (more…)
This magic quadrant focuses on what Gartner calls Structured Data Archiving. Data Archiving is used to index, migrate, preserve and protect application data in secondary databases or flat files. These are typically located on lower-cost storage, for policy-based retention. Data Archiving makes data available in context of the originating business process or application. This is especially useful in the event of litigation or of an audit.
The Magic Quadrant calls out two use cases. These use cases are “live archiving of production applications” and “application retirement of legacy systems.” Informatica refers to both use cases, together, as “Enterprise Data Archiving.” We consider this to be a foundational component of a comprehensive Information Lifecycle Management strategy.
The application landscape is constantly evolving. For this reason, data archiving is a strategic component of a data growth management strategy. Application owners need a plan to manage data as applications are upgraded, replaced, consolidated, moved to the cloud and/or retired.
When you don’t have a plan in production, data accumulates in the business application. When this happens, performance bothers the business. In addition, data bloat bothers IT operations. When you don’t have a plan for legacy systems, applications accumulate in the data center. As a result, increasing budgets bother the CFO.
A data growth management plan must include the following:
- How to cycle through applications and retire them
- How to smartly store the application data
- How to ultimately dispose data while staying compliant
Structured data archiving and application retirement technologies help automate and streamline these tasks.
Informatica Data Archive delivers unparalleled connectivity, scalability and a broad range of innovative options (i.e. Smart Partitioning, Live Archiving, and retiring aging and legacy data to the Informatica Data Vault), and comprehensive retention management and data reporting and visualization. We believe our strengths in this space are the key ingredients for deploying a successful enterprise data archive.
For more information, read the Gartner Magic Quadrant for Structured Data Archiving and Application Retirement.
Oracle DBAs are challenged with keeping mission critical databases up and running with predictable performance as data volumes grow. Our customers are changing their approach to proactively managing Oracle performance while simplifying IT by leveraging our innovative Data Archive Smart Partitioning features. Smart Partitioning leverages Oracle Database Partitioning, simplifying deploying and managing partitioning strategies. DBAs have been able to respond to requests to improve business process performance without having to write any custom code or SQL scripts.
With Smart Partitioning, DBA’s have a new dialogue with business analysts – rather than wading in the technology weeds, they ask how many months, quarters or years of data are required to get the job done? And show – within a few clicks – how users can self-select how much gets processed when they run queries, reports or programs – basically showing them how they can control their own performance by controlling the volume of data they pull from the database.
Smart Partitioning is configured using easily understood business dimensions such as time, company, business unit etc. These dimensions make it easy to ‘slice’ data to meet the job at hand. Performance becomes manageable and under business control. Another benefit is in your non-production environments. Creating smaller sized, subset databases that are fully functional now fits easily into your cloning operations.
Finally, Informatica has been working closely with the Oracle Enterprise Solutions Group to align Informatica Data Archive Smart Partitioning with the Oracle ZS3 Appliance to maximize performance and savings while minimizing the complexity of implementing an Information Lifecycle Management strategy.
What springs to mind when you think about old applications? What happens to them when they outlived their usefulness? Do they finally get to retire and have their day in the sun, or do they tenaciously hang on to life?
Think for a moment about your situation and of those around you. From the time work started you have been encouraged and sometimes forced to think about, plan for and fund your own retirement. Now consider the portfolio your organization has built up over the years; hundreds or maybe thousands of apps, spread across numerous platforms and locations – A mix of home-grown with the best-in-breed tools or acquired from the leading application vendors.
Evaluating Your Current Situation
- Do you know how many of those “legacy” systems are still running?
- Do you know how much these apps are costing?
- Is there a plan to retire them?
- How is the execution tracking to plan?
Truth is, even if you have a plan, it probably isn’t going well.
Providing better citizen service at a lower cost
This is something every state and local organization aspires to do by reducing costs. Many organizations are spending 75% or more of their budgets on just keeping the lights on – maintaining existing applications and infrastructure. Being able to fully retire some, or many of these applications saves significant money. Do you know how much these applications are costing your organization? Don’t forget to include the whole range of costs that applications incur – including the physical infrastructure costs such as mainframes, networks and storage, as well as the required software licenses and of course the time of the people that actually keep them running. What happens when those with with Cobol and CICS experience retire? Usually the answer is not good news. There is a lot to consider and many benefits to be gained through an effective application retirement strategy.
August 2011 report by ESG Global shows that some 68% of organizations had over six or more legacy applications running and that 50% planned to retire at least one of those over the following 12-18 months. It would be interesting to see today’s situation and be able evaluate how successful these application retirement plans have been.
A common problem is knowing where to start. You know there are applications that you should be able to retire, but planning, building and executing an effective and success plan can be tough. To help this process we have developed a strategy, framework and solution for effective and efficient application retirement. This is a good starting point on your application retirement journey.
To get a speedy overview, take six minutes to watch this video on application retirement.
We have created a community specifically for application managers in our ‘Potential At Work’ site. If you haven’t already signed up, take a moment and join this group of like-minded individuals from across the globe.
That tag line got your attention – did it not? Last week I talked about how companies are trying to squeeze more value out of their asset data (e.g. equipment of any kind) and the systems that house it. I also highlighted the fact that IT departments in many companies with physical asset-heavy business models have tried (and often failed) to create a consistent view of asset data in a new ERP or data warehouse application. These environments are neither equipped to deal with all life cycle aspects of asset information, nor are they fixing the root of the data problem in the sources, i.e. where the stuff is and what it look like. It is like a teenager whose parents have spent thousands of dollars on buying him the latest garments but he always wears the same three outfits because he cannot find the other ones in the pile he hoardes under her bed. And now they bought him a smart phone to fix it. So before you buy him the next black designer shirt, maybe it would be good to find out how many of the same designer shirts he already has, what state they are in and where they are.
Recently, I had the chance to work on a like problem with a large overseas oil & gas company and a North American utility. Both are by definition asset heavy, very conservative in their business practices, highly regulated, very much dependent on outside market forces such as the oil price and geographically very dispersed; and thus, by default a classic system integration spaghetti dish.
My challenge was to find out where the biggest opportunities were in terms of harnessing data for financial benefit.
The initial sense in oil & gas was that most of the financial opportunity hidden in asset data was in G&G (geophysical & geological) and the least on the retail side (lubricants and gas for sale at operated gas stations). On the utility side, the go to area for opportunity appeared to be maintenance operations. Let’s say that I was about right with these assertions but that there were a lot more skeletons in the closet with diamond rings on their fingers than I anticipated.
After talking extensively with a number of department heads in the oil company; starting with the IT folks running half of the 400 G&G applications, the ERP instances (turns out there were 5, not 1) and the data warehouses (3), I queried the people in charge of lubricant and crude plant operations, hydrocarbon trading, finance (tax, insurance, treasury) as well as supply chain, production management, land management and HSE (health, safety, environmental).
The net-net was that the production management people said that there is no issue as they already cleaned up the ERP instance around customer and asset (well) information. The supply chain folks also indicated that they have used another vendor’s MDM application to clean up their vendor data, which funnily enough was not put back into the procurement system responsible for ordering parts. The data warehouse/BI team was comfortable that they cleaned up any information for supply chain, production and finance reports before dimension and fact tables were populated for any data marts.
All of this was pretty much a series of denial sessions on your 12-step road to recovery as the IT folks had very little interaction with the business to get any sense of how relevant, correct, timely and useful these actions are for the end consumer of the information. They also had to run and adjust fixes every month or quarter as source systems changed, new legislation dictated adjustments and new executive guidelines were announced.
While every department tried to run semi-automated and monthly clean up jobs with scripts and some off-the-shelve software to fix their particular situation, the corporate (holding) company and any downstream consumers had no consistency to make sensible decisions on where and how to invest without throwing another legion of bodies (by now over 100 FTEs in total) at the same problem.
So at every stage of the data flow from sources to the ERP to the operational BI and lastly the finance BI environment, people repeated the same tasks: profile, understand, move, aggregate, enrich, format and load.
Despite the departmental clean-up efforts, areas like production operations did not know with certainty (even after their clean up) how many well heads and bores they had, where they were downhole and who changed a characteristic as mundane as the well name last and why (governance, location match).
Marketing (Trading) was surprisingly open about their issues. They could not process incoming, anchored crude shipments into inventory or assess who the counterparty they sold to was owned by and what payment terms were appropriate given the credit or concentration risk associated (reference data, hierarchy mgmt.). As a consequence, operating cash accuracy was low despite ongoing improvements in the process and thus, incurred opportunity cost.
Operational assets like rig equipment had excess insurance coverage (location, operational data linkage) and fines paid to local governments for incorrectly filing or not renewing work visas was not returned for up to two years incurring opportunity cost (employee reference data).
A big chunk of savings was locked up in unplanned NPT (non-production time) because inconsistent, incorrect well data triggered incorrect maintenance intervals. Similarly, OEM specific DCS (drill control system) component software was lacking a central reference data store, which did not trigger alerts before components failed. If you add on top a lack of linkage of data served by thousands of sensors via well logs and Pi historians and their ever changing roll-up for operations and finance, the resulting chaos is complete.
One approach we employed around NPT improvements was to take the revenue from production figure from their 10k and combine it with the industry benchmark related to number of NPT days per 100 day of production (typically about 30% across avg depth on & offshore types). Then you overlay it with a benchmark (if they don’t know) how many of these NPT days were due to bad data, not equipment failure or alike, and just fix a portion of that, you are getting big numbers.
When I sat back and looked at all the potential it came to more than $200 million in savings over 5 years and this before any sensor data from rig equipment, like the myriad of siloed applications running within a drill control system, are integrated and leveraged via a Hadoop cluster to influence operational decisions like drill string configuration or asmyth.
Next time I’ll share some insight into the results of my most recent utility engagement but I would love to hear from you what your experience is in these two or other similar industries.
Recommendations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application – especially middleware – to a business leader or even a CIO. Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts. Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts. Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data. They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.
If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how. Depending on the business model you may even have to figure out who built it or owns it. All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized. And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.
Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets. Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function. The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement. The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything. Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….
Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.
I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple. None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for. It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system. After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old. Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.
So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child? Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age. This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit. Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.
If you are aware of business models and their difficulties to leverage data, write to me. If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts. Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.