Hexaware bets on UK, new verticals to lead mid-tier IT growth

Infosys, TCS and Wipro may be getting cautious in their outlook, but not hexaware technologies

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After nine quarters of positive growth, the mid-tier leader is confident of a 20% year-on-year (yoy) growth in dollar revenues for fiscal 2013.

 

 

 

Industry Specific BI – What's the common denominator?

My previous post on business process fundamentals concluded with a friendly exhortation to BI practitioners inciting them to view their craft from the point of optimizing business process.

So the next time you are involved in any BI endeavor, please ask this question to yourself and the people involved in the project – “So which business process is this BI project supposed to optimize, why and how?” I define ‘Optimization’ loosely as anything that leads to bottom-line or top-line benefits.

Business processes by its very definition belong to the industry domain. Companies have their own business processes – some of them are standard across firms in that particular domain and many of them are unique to specific companies. Efficiency of business processes is a source of competitive advantage and the fact that ERP vendors like SAP has special configurations for every industry illustrates this point. So by corollary, for BI to be effective in optimizing business processes, it has to be tied to specific industry needs creating what can be called as “Verticalized Business Intelligence”. (V-BI in short)

At Hexaware’s Business Intelligence & Analytics practice (the company and team that I belong to), we have taken the concept of V-BI pretty seriously and have built solutions aimed at industry verticals. You can view our vertical specific BI offering at this link and we definitely welcome your comments on that.

Though Verticalized BI is a powerful idea, companies typically need an “analytics anchor point” to establish a BI infrastructure before embarking on their domain specific BI initiatives. The analytics anchor point, mentioned above, should have the following characteristics:

• All organizations across domains should have the necessity to implement it
• Business process associated with these analytics needs to be fairly standardized and should be handled by experts
• Should involve some of the most critical stakeholders within the organization as the success of this first initiative will lay the foundation for future work

Based on my experience in providing consulting services for organizations in laying down an Enterprise BI roadmap, I feel that “Financial Analytics” has all the right characteristics to become the analytics anchor point for companies. Financial Analytics, the common denominator, typically comprises of:

• General Ledger Analysis – (also known as Financial Statements Analysis)
• Profitability Analysis (Customer / Product Profitability etc.)
• Budgeting, Planning & Forecasting
• Monitoring & Controlling – The Dashboards & Scorecards
• General Ledger Consolidation

The above mentioned areas are also classified as Enterprise Performance Management. The convergence of Performance Management and BI is another interesting topic (recent announcements of Microsoft have made this subject doubly interesting!) and I will write about it in my future posts.

In my humble opinion, the prescription for Enterprise BI is:

• Select one or more areas of Financial Analytics (as mentioned above) as your first target for Enterprise BI.
• During the process of completing step 1, establish the technology and process infrastructure for BI in the organization
• Add your industry specific BI initiatives (Verticalized Business Intelligence) as you move up the curve

I, for one, truly believe in the power of Verticalized BI to develop solutions that provide the best fit between business and technology. That business and IT people can sit across the table and look at each other with mutual respect is another important non-trivial benefit.

Thanks for reading. Do you have any other analytics anchor points for organizations to jumpstart their BI initiatives? Please do share your thoughts.

Read More About  Industry Specific BI

Analytics, choosing it

We observe many BI Project Sponsors clearly asking for an Analytics Package implementation to meet business needs; the benefit is that it saves time. By deciding on an analytics package we can get the application up quickly and comes with all typical benefits of a ‘buy’ solution against a ‘build’ solution.

So what are the key parameters that we need to look for in choosing an Analytics Package. The following would be the points to consider in choosing an Analytics Package, in the order of importance.

1.The effort to arrive at the right data model for a BI system is huge and as well quite tedious, so a comprehensive ‘Data Model & Metrics, Calculations’ from the package is very important.

2.The flexibility and the openness in managing Data Model is also very critical, some of tools to manage the data model elements that can be looked for are

• Ability to browse the data elements and its definitions
• Support for customization of the data model without getting back to the database syntax
• Auto Source System profiling and field mapping from the source systems to the data model
• Enabling validation of data type, data length of the data model against the source system field definitions
• Means to ensure that customization of the data model in terms of field addition doesn’t happen when a similar element exists
• Availability of standard code data as applicable to the functional area
• Supporting country specific needs in terms of data representation

3. ETL process for a BI system is also a major effort. Though the absolute effort of pulling the data and making it available for the package in the required format cannot be avoided, availability of plug-ins that can understand the data structure from typical systems like ERP would save good amount of effort.

4. Availability of ETL process for typical data validation as part of ETL is also a must; integration with any data quality product would be valuable

5. Ability to support audit and compliance requirements for data usage and reporting

6. Integration of the package with industry specific research data from vendors like D&B, IMS etc to enable benchmarking the performance metrics against industry peers/competitors

7. Customizable Security Framework

8. Semantic layer definition with formulas, hierarchies etc

9. Ready to use Score Cards and dashboard layouts

10. Pre built reports and portal

Often all the pre delivered reports under go changes and are almost completely customized when implemented. So availability of a larger list of reports itself doesn’t mean a lot since most of the reports would be minor variations from one other. Certain compliance reports would be useful when it comes along with the package; these would be published industry standard report formats.

Definitely an evaluation phase to test the Analytics products capability on a sample of the data before choosing it is a must, the above ten points would the evaluation criteria during this exercise.

Business Process for BI Practitioners – A Primer

Business Intelligence has a fairly wide scope but at the fundamental level it is all about “Business Processes”. Let me explain a bit here.
BI, without the bells and whistles, is about understanding an organization’s business model, its business processes and ultimately find the reason (analytics) and way to optimize the processes. The actions are carried out based on informed judgments (aided by BI), to make the organization better in whatever endeavor it has set itself to accomplish.

Assuming that BI practitioners are convinced that understanding business process is critical to their work, let me delve a bit into the basics of it.

1) What is a business process? (As a side note, one of the best explanation for business models is given by Joan Magretta in her book ‘What Management Is”)

Business processes are set of activities involved within or outside an organization that work together to produce a business outcome for a customer or to an organization. The fact is that for an organization to function, there are many outcomes that are required to happen on a daily basis.

2) What are BPM Tools?

Business Process Management (BPM) tools are used to create an application that is helpful in designing business process models, process flow models, data flow models, rules and also helpful in simulating, optimizing, monitoring and maintaining various processes that occur within an organization.

3) The Mechanics of Business Modeling

Business Process Modeling is the first step, followed by Process Flow Modeling and Data Flow diagrams. All these 3 diagrams and associated documentation will help in getting the complete picture of an organization’s business processes. Brief explanation of these 3 types are given below:

a) In Business Process Modeling, an organization’s functions are represented by using boxes and arrows. Boxes represent activities and arrows represent information associated with that activity. Input, Output, Control and Mechanism are the 4 types of arrows. A box and arrows combination that describes one activity is called a context diagram and obviously there would be many context diagrams to explain all the activities within the enterprise.

b) Process Flow Modeling is a model that is a collection of several activities of the business. IDEF3 is the process description capture method and this workflow model explains the activity dependencies, timing, branching and merging of process flows, choice, looping and parallelism in much greater detail.

c) Data Flow Diagrams (DFD) are used to capture the flow of data between various business processes. DFD’s describe data sources, destinations, flows, data storage and transformations. DFDs contains five basic constructs namely: activities (processes), data flows, data stores, external references and physical resources.

Just like the data modeler goes thro’ conceptual, logical and physical modeling steps, a business process modeler creates the Business Process Models, Process Flow Models and Data Flow Diagrams to get a feel for the business processes that take place within an enterprise.

Thoughts for BI Practitioners:

1. Consider viewing BI from the point of optimizing business processes
2. Might be worthwhile to learn about Business Process Modeling, Process Flow Modeling and Data Flow Diagrams
3.  
4. Understand the working of BPM tools and its usage in the enterprise BI landscape
5. Beware of the acronym BPM. BPM is Business Process Management but can also be peddled as Business Performance Management.
6.  
7. My view is that Performance Management is at a higher level, in the sense, that it is a collective (synergistic) view of the performance of individual business processes. A strong performance management framework can help you drill-down to specific business processes that can be optimized to increase performance.

Analytics, its Evolution

What is ‘Analytics’ – A business intelligence application with ready to use components for data analysis, we also refer to it as ‘packaged analytics’. ‘Business Analytics’ refers to analytics applications that support analysis of data collected as part of a business process.

In similar lines we can define an analytics application that supports analysis of data collected as part of a ‘computer user’ daily activity as ‘Personal Analytics’.

Business systems evolved from the state of building custom applications to a state of configurable generic Enterprise Resource Planning (ERP) systems. Now we have configurable generic business intelligence applications called ‘Business Analytics’ which have evolved from the state of building custom business intelligence applications.

The ERP systems are designed to collect the business data where as the Business Analytics systems are designed to analyze the collated business data, so one of the key sources for a Business Analytics application is an ERP system. Data analysis is a next logical step after data collection, the ERP vendors like Oracle, SAP, Microsoft got delayed in addressing this specific requirement of data analysis. In the last two years we have seen some finer business intelligence products being acquired by the ERP vendors. Clearly the customers who are on ERP products would get a better platform that can talk to their ERP applications for data analysis.

It’s a reality that not many companies, at least the larger (>USD 500million) companies would not run their entire business in one ERP system. Consolidating all applications to one single ERP platform will not happen immediately, multiple ERP and custom applications would get added if the company grows through acquisitions, hence existence of multiple transaction systems cannot be avoided. The number of customers embracing packaged analytics from the ERP vendors will increase as the flexibility of the business analytics applications from the ERP vendors matures to accept data from other outside applications.

Logical Data Model to Packaged Reports

The business analytics applications grew step by step as following

• 1. Logical data model – as a first step towards the formation of packaged analytics, companies like IBM, Teradata provided industry specific logical data models (LDM) to help customers build their enterprise data warehouse. The LDM was based on the business process and provided the required jumpstart to enable the integration of data from multiple source systems effectively. We also have certain industry endorsed LDMs like Supply-Chain Operations Reference-model (SCOR), Public Petroleum Data Model(PPDM
• 2. Metrics definition – LDMs led to the next step of defining metrics to measure the performance of the business process. The required data for the metrics that were specific to a business process were extracted (virtually/physically) into data marts as analytic data models in a fact-dimension data model
• 3. Semantic Layers – the next step was the creation of semantic layer over the data mart to enable adhoc querying and report generation
• 4. Reports and Dashboards – then we had set of reports and dashboards delivered over the semantic layer

Still the packaged analytics are positioned as a data mart application addressing specific business process like HR or Customer Relationship, unlike ERP systems which addresses complete end to end business process of an organization…there is still more time to go for an Enterprise Analytics Application to be established.

Read More About  Analytics and its Evolution