Data Drives Everything: Do you know where your data is and how to use it?

Posted by David Fellers on Wed, May 16, 2018 @ 04:33 AM

For the past forty years, companies have been building all their financial and business decisions around information contained in traditional data structures. Virtually all financial systems, no matter who has designed them, have been built around relational databases.  This brings the same set of issues and challenges to every single system.

The primary limiting factor with every relational database is a relatively finite limit as to how much data it can contain without compromising performance.  In most companies, these finite relational database limits have already been outstripped by the flood of new data that is constantly being created.  In response, virtually all companies have resorted to segmentation of their information into different standalone databases for faster manipulation and management. 

For example, the General Ledger is often separated from sub-ledgers and other critical functions such as profitability analysis, revenue recognition and management reporting are relegated to standalone programs and dedicated servers.


Although the goal of segmentation is to achieve faster performance from each database, every time a function or data set is handled separately, informational gaps are created. This sets in motion a myriad of problems from different understandings of what is supposedly the same information based on where and how the data is stored.  The result is a need for constant reconciliation of data drawn from different databases and the imposition of rigid “data cleansing” processes before any decisions can be made.

Of course, these interim steps to cleanse and validate the data are cumbersome, time consuming and a big drain on productivity.  Managers can sometimes spend much more time debating the accuracy of each other’s data than they actually spend on making decisions.

In recent years, thought leaders have been grappling with the above issues in an effort to find effective ways of unifying these separate data repositories with the core processing of transactions.  Overall goals have been to increase accuracy, efficiency and performance by eliminating the need to massage, analyze, reconcile and move data before acting on it, while at the same time accelerating transaction speed.  Steady improvements in chip performance, memory speeds and in-memory software architectures have now made those goals achievable.

By leveraging the in-memory data and processing capabilities of the SAP S/4HANA architecture and S/4HANA Cloud deployment scenarios, CFOs can seamlessly unify their information landscape to remove the gaps and ease the pain-points arising from the artificial segmentation of information.

Instead of always grappling with reassembling disparate pieces of the picture this approach enables CFOs and staff throughout the company to see a holistic real-time view that encompasses all operational data sets and analysis capabilities within a single unified architecture.

In addition to improving both the access to and the ability to manipulate information, S/4HANA also dramatically improves real-time analytics performance because nothing must be moved, massaged or reconciled before the analysis.

With S/4 HANA you now have access to all the data in real time. So how does this change the paradigm for how we approach data? In the past we struggled to get data. You had to abstract it, put it into files and then use what you had. I know that as a user of data we had to think long and hard about what data we needed to answer specific questions.

The heart of this new unified approach is the Universal Journal, which now enables the long-sought after "Single-Source-of-Truth" for decision making. It embodies the concept that all the data is unified within an unsegmented database so that all processes and people see the same set of information in real time. 

This is possible because S/4HANA offers three transformative innovations:

  1. Enables a 7 to 10 times compression of data to greatly shrink the data footprint
  2. Takes the data off disk and puts it into main memory so it’s immediately available
  3. Makes real-time information available to front-line users via flexible S/4HANA deployment scenarios and personalized User Interface technologies

Leveraging S/4HANA has enabled a complete re-imagining of the whole financial architecture and structure of applications so that everything can be driven in real time from the single SAP Intelligent Digital Core.


Once everyone is working with the same information and operating in sync with each other, the single-source-of-truth also begins to inform high-level strategies and long-term planning processes.   Instead of constantly struggling to understand where you are, the whole team can turn their focus and energies toward getting where you want to be.

Now that we have instant access to raw transaction level data, and are constantly adding more data with technologies such as Internet of Things (IoT) and machine learning, companies need partners, such as Bramasol, who are experts in deploying solutions with the agility and scalability to handle escalating data and transaction processing requirements.

Bramasol is a co-innovation leader in the implementation of the SAP S/4HANA (On-Premise and Cloud) to help customers achieve high-performance results with in-memory capabilities and extensible Digital Core. 

Our capabilities include leveraging S/4HANA technology in purpose-built offerings such as our Rapid RevRecReady Compliance Solution and Rapid Leasing Compliance Solution as well as helping our customers incorporate S/4HANA benefits into their on-going Finance Innovation initiatives.

Click here to request a demo of S/$HANA Cloud and get expert answers to your specific needs.

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Topics: data integration, s4/hana cloud

e-healthcare in India

Posted by Poorvi Sharma on Wed, Sep 4, 2013 @ 01:00 PM

Govt moves to roll out ambitious e-health plan

The health department has invited expression of interest for its ambitious e-health project, which envisages an electronic health card for all the people who seek treatment in government hospitals across the state.

The expression of interest has been invited from the entities for integrated e-health solutions covering the entire health sector of the state. It would capture the demographic data, automate hospital process and bring all information into a centralized state health information system through the network to ensure continuity in health care.




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Topics: predictive analytics, Bramasol India, data integration, ERP

Predictive and Data Mining is the Next Wave of Analytics Democratization

Posted by Ron Martinez on Tue, Apr 9, 2013 @ 05:59 AM

predictive resized 600Ok, full disclosure up front: I am a big proponent of Data Mining and Predictive Modeling as being the next logical step in the evolution of business intelligence and decision support capabilities for the business user. So it should not be a surprise that my reaction to the recent CIO Magazine webcast on SAP’s new Predicative Analysis software solution is enthusiastic.

The premise is this: what was once a highly sophisticated and somewhat arcane analytic discipline that belonged to the Statistics PhDs is now being made available to the BI Power User in the business functions. Data Mining has always attempted to move beyond the retrospective, hypothesis-driven world of traditional query, reporting and analysis by using mathematical algorithms to automatically find patterns and relationships in the data. Large Fortune 500 companies have been using the technology for many years to do things like perform Market Basket analysis, segment customers, and model customer attrition. It’s just been too expensive and complex for any but the largest organizations to leverage.

SAP Predictive Analysis has a few important things going for it from my perspective. First, it was designed from the ground up to be fully integrated with the data integration and visualization capability delivered by SAP Visual Intelligence (a business-user BI app). Secondly, it integrates the open source R language. R is a free programming language and a software environment for statistical computing. It is fast on the rise and making an impact in a space that has been historically dominated by SAS and IBM. Lastly, SAP PA is tightly integrated with SAP’s breakthrough in-memory database, HANA. This allows the solution to crunch through massive volumes of data in-database, as opposed to having to move the data into a separate data store for Data Mining purposes. This speeds both compute time and total cycle time, delivering the possibility of running things like predicative churn modeling continuously, allowing organizations to capture opportunities at that brief, critical moment of customer engagement.

What I liked about the webcast is that it focused the discussion on the business user. When we take a powerful capability like Predictive and make it accessible to the business, we have the recipe for a significant forward leap in the business value we can extract from our business data. I have some real-world experience with Data Mining over a decade ago, and I always considered it to be an incredibly powerful, yet under-utilized capability. When we arm our best and brightest BI Power Users with an effective way to wield this technology, the possibility of unlocking massive incremental business value is real.


Want to know more? Click to button to read the Forrester whitepaper: Big Data Predictive Analytics Solutions.

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Topics: predictive analytics, business intelligence, Forrester, predictive analysis, dashboard, analytics, data integration, webinar, HANA, SAP, big data

Webinar Wrap Up: SAP-Managing Big Data as Enterprise Information

Posted by Dave Reclite on Fri, Apr 5, 2013 @ 08:46 AM

I recently attended a webinar sponsored by SAP.  The topic was Managing Big Data as Enterprise Information.  There were two parts to this webinar: the first, a conceptual presentation around Hybrid Data Ecosystem by Shawn Rogers from EMA; the second, Marie Godell explained how SAP solutions support the Hybrid Data Ecosystem.

Shawn presented the concept of a Hybrid Data Ecosystem. Basically this is a multiple platform (OTLP, EDW, Hadoop, NoSQL .. .) environment providing data to satisfy business requirements.   The Hybrid Data Ecosystem can address requirements within these five categories:

  • Economics - The tipping point of ROI vs investment.
  • Load - Mix of data types and sources, adding challenge and value to the environment.
  • Structure - Data source organization, models versus late binding and additional uses.  Schema flexibility.
  • Analytics - Complexity of workload.  Managing and overcoming obstacles of traditional systems.
  • Response - Speed to scale, speed to answer.  Stretching the boundaries of traditional systems.

 A Hybrid Data Ecosystem differs from the traditional enterprise systems, such as data warehouses, in that they have a central Analytic Engine which utilizes the strengths of the source systems. Instead of constantly loading all the data into a central EDW, a Hybrid model would leverage the source system to do the queries and data manipulation that they do best. You would only need to move data to the Analytic Engine when there is a clear advantage to having that data in this system.   

In order for a Hybrid Data Ecosystem to succeed, you need to have the proper tools in place.  The foundational pieces are the Data Integration Tier and the Data Management Tier. The Data Integration Tier may utilize existing ETL, Replication or other data integration tools, but as this ecosystem grows more complex you may need to look to the new product stacks being developed by the various Application and DB vendors. Data Management is key to leveraging data from various sources, keeping the data consistent and accurate. This can be a challenge in environments without strong data governance and/or siloed applications.

Shawn predicts that the Hybrid Data Ecosystem will only grow, both in complexity and adoption going forward. Companies will benefit from embracing the system sooner than later; putting the proper foundational components in place will position them for future growth.

In the second part of the webinar, Marie Godell presented the SAP solution set addressing the Hybrid Data Ecosystem. She outlined the need for solutions to address the need for Operational Efficiencies, Better Business Decisions and Risk Reduction. She explained how the SAP Real-time Data Platform addresses these areas.


saprealtime resized 600 

As you can see from the diagram above, SAP’s approach is to have SAP HANA act as the Analytic Engine in the Hybrid Data Ecosystem. Not only can it support SAP applications, but also integrates with other technologies like Hadoop. In addition to SAP HANA, SAP also has its Enterprise Information Management (EIM) tools. SAP’s EIM includes integration tools (SLT, Data Services, Replication) to provide a solid foundation for integrating the various components. 

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Topics: analytic engine, EDW, OTLP, NoSQL, EIM, Hybrid Data Ecosystem, Hadoop, analytics, SAP HANA, data integration, webinar, SAP, big data

See Far Into the Future with Predictive Analytics

Posted by Neeraj Dharia on Fri, Mar 22, 2013 @ 09:08 AM

Predictive Analytics


How are forward-thinking companies seeing buiness impacts six months, a year, or even further down the road? The answer: predictive analytics.

As of 2010, Forbes called predictive analytics a “game-changer.” Today, predictive analytics provide the greatest opportunity for delivering high value results from investment in analytics.  Coupled with the big data processing capabilities of SAP HANA, more organizations are now able to access this sophisticated analytic capability.
Information Management released their top 5 trends in predictive analytics. These trends include:

• Providing Solutions Across the User Spectrum: More and more business users are becoming common consumers of predictive analytics output. As a consequence, the software is being adapted for more business user-friendly interaction.

• Operationalizing Models: Predictive models are being incorporated into business processes so everyday tasks and scenarios can be executed with even greater efficiency.

• Supporting Unstructured Data: End users realize they can gain significant insight from mining unstructured (i.e., text) data generated by social networks like Facebook and Twitter, which, used in conjunction with structured data, can provide more accurate predictive models.

• BIG DATA: Real-time analysis of large amounts of data is also becoming more prevalent. For example, companies might use this capability for real-time ad placement. Or, a manufacturer can gain early warning of potential supply chain disruptions in real-time.

• Open Source: Open source solutions are becoming increasingly important to the predictive analytics market because they enable a wide community to engage in innovation.  SAP Predictive Analysis integrates the open source R statistical language.

Predictive analytics allows different lines of business (LOBs) to not only understand what happened and why, but also to predict what will happen next.  After all, businesses don’t have the budget to keep pouring their resources into unnecessary expenditures.  With a more reliable predictive capability, LOBs can see where they are spending and what they need to do to improve their results. On a bigger picture level, organizations can predict failures in their infrastructure and take pre-emptive action before the problem occurs.

Need a little more info on how to get more from your data? Download the whitepaper from Aberdeen: Data Management for BI- Getting Accurate Decisions for Big Data.

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Topics: Aberdeen, analytics, SAP BusinessObjects, packaged analytics, dashboards, scorecards, SAP HANA, data integration

Build the Foundation for Analytics Integration

Posted by Neeraj Dharia on Fri, Mar 15, 2013 @ 11:12 AM

Business Objects integration

Mid-year budgeting and adjustment periods are quickly closing in on us, as we anxiously watch results from the first quarter start to roll in. How did we do? Are we on track? Where is our biggest opportunity?  These are some of the questions you are likely starting to ask.

It’s one thing to add Business Intelligence to your IT shopping cart. It’s another thing entirely to know whether you have the right pieces in place to effectively support your BI solution. One of the many advantages that Bramasol—an SAP Gold Partner and Value Added Reseller for SAP—brings is complementing the sophistication of BusinessObjects with a rock-solid data integration plan. We have SAP-certified experts on staff who have tackled some of the most complex data integration challenges, and they’ll use proven SAP solutions, methodology, and best practices to help your business Ignite Possible.

A system such as SAP BusinessObjects takes volumes of organizational data and transforms it into the right information, including dashboards, scorecards and web-based reports. Since data is housed in many different locations, it can be more difficult to access, integrate and consolidate quickly and efficiently without the use and proper implementation of data integration and BI software.

According to a report from EBIZ—the insider’s guide to next generation BPM—data comes in many different formats, including:

  • Cloud-based data
  • Legacy data
  • Spreadsheet data
  • Disparate databases
  • Data from operational applications
  • Data from SaaS applications

All of these different data sources need to be cleansed, remediated, matched and merged before being effectively leveraged by your users.

With the right formula to succeed, your organization will gain the most value from your BI investment. Aberdeen recently published a report: Packaged Analytics, The Gift that Keeps on Giving. It provides you with more insight into why analytics, why now and how easy it can be to get started and derive value rapidly. Click below to download the whitepaper!

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Topics: Aberdeen, analytics, SAP BusinessObjects, packaged analytics, dashboards, scorecards, SAP HANA, data integration, SAP

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