Category Archives: Application Retirement
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.
In my last blog on this topic, I discussed several areas where a database archiving solution can complement or help you to better leverage the Oracle In-Database Archiving feature. For an introduction of what the new In-Database Archiving feature in Oracle 12c is, refer to Part 1 of my blog on this topic.
Here, I will discuss additional areas where a database archiving solution can complement the new Oracle In-Database Archiving feature:
- Graphical UI for ease of administration – In database archiving is currently a technical feature of Oracle database, and not easily visible or mange-able outside of the DBA persona. This is where a database archiving solution provides a more comprehensive set of graphical user interfaces (GUI) that makes this feature easier to monitor and manage.
- Enabling application of In-Database Archiving for packaged applications and complex data models – Concepts of business entities or transactional records composed of related tables to maintain data and referential integrity as you archive, move, purge, and retain data, as well as business rules to determine when data has become inactive and can therefore be safely archived allow DBAs to apply this new Oracle feature to more complex data models. Also, the availability of application accelerators (prebuilt metadata of business entities and business rules for packaged applications) enables the application of In-Database Archiving to packaged applications like Oracle E-Business Suite, PeopleSoft, Siebel, and JD Edwards
- Data growth monitoring and analysis – available in some database archiving solution to enable monitoring and tracking of data growth trends and the identification of which tables, modules, and business entities are the largest and fastest growing to focus your ILM policies on.
- Performance monitoring and analysis – also available in some database archiving solution — allows Oracle administrators to easily and more meaningfully monitor and analyze database and application performance. They can identify the root cause of performance issues, and from there, administrators can define smart partitioning policies to segment data (i.e. mark them as inactive) and monitor the impact of the policy on improving query performance. This capability helps you to identify which set of records should potentially be “marked as inactive” and segmented.
- Automatic purging of unused or aged data based on policies – database archiving solutions allow administrators to define ILM policies to automate the purging of records that are truly no longer used and have been in the inactive state for some time.
- Optimal data organization, placement, and purging, leveraging Oracle partitioning – a database archiving solution like Informatica Data Archive is optimized to leverage Oracle partitioning to optimally move data to inactive tablespaces, and purge inactive data by dropping or truncating partitions. All of these actions are automated based on policies, again eliminating the need for scripting by the DBA.
- Extreme compression to reduce cost and storage capacity consumption – up to 98% (90%-95% on average) compression is available in some database archiving solutions as compared to the 30%-60% compression available in native database compression.
- Compliance management – Enforcement of retention and disposal policies with the ability to apply legal holds on archived data are part of a comprehensive database archiving solution.
- Central policy management, across heterogeneous databases – a database archiving solution helps you to manage data growth, improve performance, reduce costs, ensure compliance to retention regulations, and define and apply data management policies across multiple heterogeneous database types, beyond Oracle.
Under the hood: decommissioning an SAP system with Informatica Data Archive for Application Retirement
If you reached this blog, you are already familiar with the reasons why you need to do a house cleaning on your old applications. If not, this subject has been explored in other discussions, like this one from Claudia Chandra.
All the explanations below are based on Informatica Data Archive for application retirement.
Very often, customers are surprised to know that Informatica’s solution for application retirement can also decommission SAP system. The market has the feeling that SAP is different, or “another beast”. And it really is!
A typical SAP requires software licenses, maintenance contracts, and hardware for the transactional application itself, the corresponding data warehouse and databases, operating systems, server, storage, and any additional software and hardware licenses that you may have on top of the application. Your company may want to retire older versions of the application or consolidate multiple instances in order to save costs. Our engineering group has some very experienced SAP resources, including myself here, with more than 16 years of hands-on work with SAP technology. And we were able to simplify the SAP retirement process in a way that makes the Informatica Data Archive solution decommission SAP as any other type of application.
Next are the steps to decommission an SAP system using Informatica Data Archive.
Let’s start with some facts: SAP has some “special” tables which can only be read by the SAP kernel itself. In a typical SAP ECC 6.0, around 9% of these tables fall in these categories, representing around 6,000 tables.
More specifically, these tables are known as “clusters”, “pools” and I created a third category with transparent tables which have a binary column, or RAW data type, which only SAP application can unravel.
In this step, we will get all the metadata of the SAP system being retired, including all transparent, cluster and pools tables, all columns with data types. This metadata will be kept with the data in the optimized archive.
2) Extraction from source
Informatica Data Archive 6.1.x is able to connect to all database servers certified by SAP, to retrieve rows from the transparent tables.
On the SAP system, it is required to install an ABAP agent, which has the programs developed by Informatica to read all the rows from the special tables and archive files and to pull all the attachments in its original format. These programs are delivered as an SAP transport, which is imported in the SAP system prior to the beginning of the decommissioning process.
Leveraging the Java connector publicly available through the SAP portal (SAPJCo), Informatica Data Archive connects to an SAP application server on the system being decommissioned and make calls to the programs imported though the transport. The tasks are performed using background threads and the process is monitored from the Informatica Data Archive environment, including all the logging, status and monitoring of the whole retirement process happening in the SAP system.
Extraction of table rows in database
Below you can see what all SAP table types are and how our solution deals with it:
|Table type||Table name in SAP
|Table name in the database(Physical table)||How we handle it?|
|Cluster tables||BSEG||RFBLG||The engine reads all the rows from the logical tables by connecting to the SAP application level and store in the archive store as if the table existed in the database as a physical table.The engine also reads all rows of the physical tables and stores as they are, as a policy insurance only, since the data cannot be read without an SAP system up and running|
|Transparent tables with RAW field||PCL2STXL||PCL2STXL||The engine creates a new table in the archive store and read all rows from the original table, but the RAW field is unraveled.The engine reads all rows of the physical tables and store as they are, as a policy insurance only, since the data cannot be read without an SAP system up and running
The engine also reads all rows of the original table PCL2 or STXL and stores as they are, as a policy insurance only, since the data cannot be read without an SAP system up and running
The Informatica Data Archive will extract the data of all tables, independently of their types.
Table rows in archive files
Another source of table rows is the archived data. SAP has its own archiving framework, which is based on a creation of archiving files, also known as ADK files. These files store table rows in an SAP proprietary compacted form, which can only be read by ABAP code running in a SAP system.
Once created, these files are located in the file system and can be stored in an external storage using an ArchiveLink implementation.
The Informatica Data Archive engine also reads the table rows from all ADK files, independent of their location, as long as the files are accessible by the SAP application being retired. These table rows will be stored in the archive store as well, along with the original table.
Very important: After the SAP system is retired, any implementation or ArchiveLink can be retired as well, along with the storage that was holding the ADK files.
Business transactions in SAP systems have the ability to have attachments linked to them. The SAP Generic Object Services (GOS) is a way to upload documents, add notes to a transaction, add URLs relevant to the document, all still referencing a business document, like a purchase order or a financial document. Some other SAP applications, like CRM, have its own mechanism of attaching documents, complementing GOS features.
All these methods can store the attachments in the SAP database, or at SAP Knowledge Provider (KPro) or externally in storages, leveraging an ArchiveLink implementation.
Informatica’s engine is able to download all the attachment files, notes and URLs as discrete files, independent of where they are stored, keeping the relationship to the original business document. The relationship is stored in a table created by Informatica in the archive store, which contains the key of the business document and the link to the attachments, notes and URLs that were assigned to it in the original SAP system.
All these files are stored in the archive store, along with the structured data – or tables.
4) Load into optimized archive
All data and attachments are then loaded into Informatica’s optimized archive,. The archival store will compress the archived data up to 98%
5) Search and data visualization
All structured data are accessible though JDBC/ODBC, as any other relational database. The user has the option to use the search capability that comes with the product, which allows users to run simple queries and view data as business entities.
Another option is to use the integrated reporting, capability within the product, which allows users to create pixel-perfect reports, using drag and drop technology, querying the data using SQL and displaying the data as business entities, which are defined in prebuilt SAP application accelerators. .
Informatica also has a collection of reports for SAP to display data for customers, vendors, general ledger accounts, assets and financial documents.
Some customers prefer to use their own corporate standard 3rd party reporting tool. That is also possible as long as the tool can connect to JDBC/ODBC sources, which is a market standard for connecting to databases.
Hopefully this blog helped you to understand what Informatica Data Archive for Application Retirement does to decommission an SAP system. If you need any further information, please comment below. Thank you.