Enterprises are increasingly operating in a dynamically changing and fluid environment. They are constantly changing gears just to keep pace. CXOs are constantly looking for ways to overcome or create disruptions in a world becoming increasingly complex. Infosys Consulting Blog gathers a community of subject matter experts who are driving pragmatic conversations around that which is changing and that which needs to be rethought, redefined and redesigned for enterprises to achieve market-leading performance roadmaps.

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August 29, 2012

How Can Companies Realize Business Intelligence ROI Faster?

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If you ask any group of business executives, they will be nearly unanimous when it comes to vocalizing their need to use analytics to make better business decisions.  However, ask the same executives whether or not the actual ROI they get out of large business intelligence initiatives meets or exceeds the hype, and you will usually get a very different answer.  Unfortunately, in the eyes of the business users, many view business intelligence as a "money pit" they can't afford not to invest in...


Obviously, there can be many reasons why a business intelligence program either fails or isn't perceived to be as successful as originally promised.  Just like any other IT-enabled business transformation initiative.  For example, they can fail because of poor project management, bad requirements, poor scope control, inaccurate linkage of requirements to business value, etc.  Above and beyond the usual causes, however, many business intelligence projects fail because of reasons specific to the discipline itself.   To understand this point, it is important to understand the typical approach a majority of IT organizations take when building a business intelligence solution. 

 
Following approval and funding to move forward, IT organizations ultimately spend an inordinate period of time designing and building application logic to integrate data from multiple sources (typically called ETL, or 'Extract, Transform, Load') into the data warehouse.  Unfortunately, until this phase is complete and the data is consolidated, only minimal business value can be realized.   That is because the most powerful analytics are frequently those that require the data to be integrated from a multitude of sources.  So in essence, this traditional approach mandates that IT goes away for 6 -12 months (or longer sometimes), builds the ETL to integrate the data.  Only then can reports and visualizations add value. 

 
Why is this bad?  First, because business conditions and priorities change in a flash in today's global marketplace, forcing companies to change strategic direction much faster than ever before.   For example, transitioning from being 'product-focused' to 'customer-focused', adapting to technology innovation, expanding across a value chain, or completing acquisitions radically impact what kinds of analytics a company needs for better business decision-making.  Second, even without changes in business strategy, changes in management and changing org structures introduce new decision-makers into the project.  Unfortunately, new decision-makers frequently have different priorities than their predecessors.


So in essence, just like in many sports, 'speed kills'; or maybe more appropriately in this case, 'the lack of speed kills'.  To solve this problem, many advocate   using an iterative approach such as an AGILE methodology.  At Infosys, we agree that leveraging an iterative methodology is part of the solution.  However, it cannot be the entire solution.  This is NOT a problem that can be solved by methodology alone.  This is where 'Data Virtualization' comes into play. 


Leading vendors such as Informatica, IBM, Composite, and Microsoft have built very robust platforms.  If you aren't familiar with data virtualization and you are in the business intelligence space, you really should look into using these types of tools.  While they do not eliminate the need for traditional approaches, our experience is that the amount of ETL can frequently be reduced by 50%.  For example, for a major financial services client of ours, we were able to help them reduce the total effort for data integration (ETL) by 200+% and cut the development in half versus traditional methods.


The end result is that the length of time required to deliver business value is dramatically reduced, giving business and IT teams more time to deliver cutting-edge data visualizations - which is where the value gets realized! 
What do you think?  We'd love to hear your comments - good or bad!

August 28, 2012

Defining the Right Metrics for Long-Term Business Transformation

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Companies often develop business cases for transformational IT initiatives to identify quantifiable implementation areas, focusing on business value to justify funding.  Such focus, however, typically doesn't last beyond the project approval phase, having little or no impact on the remaining lifecycle.  This raises the question, how can management ensure that the value defined in the business case is in fact realized through the business transformation? 

A key component of value realization is metrics management.  This essentially means selecting the right key performance indicators (KPIs) during the business case exercise, refining them based on the engagement context, and then tracking the right subset of metrics throughout the project and beyond.  On a recent HCM ERP implementation, we defined just such a metrics management plan for the in-scope process areas of the engagement. 

The primary goal was to provide a forward-looking governance model for HCM KPI management to ensure future value realization and minimize risk.  During the metrics management exercise, we selected KPIs from the business case, mapped them to the primary in-scope HR processes, and prioritized the metrics based on ease of implementation, relative business value, and engagement impact.  We then held multiple review sessions with corporate leadership to further refine the metrics and define an effective governance model for short- and long-term management across the major processes.

Creating an effective KPI management plan often involves several key activities:

1. Define the major value themes, pain points, and opportunity areas for your client or business.  Articulate the major value areas that will be impacted by the ERP-enabled engagement as well as how specific components can be improved to drive revenue growth or cost savings.  Define the relationship between these components and specific process areas as well as change initiatives.  At Infosys, identifying and illustrating these relationships is fundamental to how we help clients realize business value from IT.

2. Define the most relevant KPIs for the key areas of improvement and quantify them through a business case.  Based on the findings of the business case, refine the metrics to include in the metrics management plan.

3. Leverage the above information to establish a clear metrics management plan and socialize the metrics with key stakeholders.  The metrics should include both strategic and operational items, as well as others that are relevant in the short-term and steady state.  The right mix will depend on the engagement and the maturity of the in-scope processes.  The plan should also include a clear governance strategy to establish short- and long-term ownership and accountability as the processes mature.

4. If information is available, identify baselines and determine target values.  In many cases, metric data may not be available for the business, hence the need for a metrics management plan.  If this is the case, leverage industry benchmarks for reference and keep these in mind as you start collecting data through the enterprise dashboard (step 5).  Once you have a sense of where your company stands, define targets and revise the incentive structure of metric owners to encourage metric improvement. 

5. Enable real-time tracking of metrics through an enterprise dashboard.  The dashboard should be configured to allow specific user groups to access the right metrics and associated data based on their needs (for example, their process areas, business unit, etc.). 

What are traits of a good metric?  There a few simple rules to keep in mind.  Generally, the metric should be: 1. actionable (there is a clear response for change in metric value), 2. have a common interpretation (there is a uniform and consistent metric definition), 3. have credible, accessible data (data acquisition involves reasonable effort from trusted sources), and 4. have a transparent, simple calculation (there is a clear understanding across stakeholders).

Remember that you cannot manage what you do not measure.  By selecting the right set of KPI's for your metrics management plan, defining clear governance, and enabling tracking through a dashboard, you will have a much better idea of whether your transformation is truly a success and be able to proactively address potential problems before they materialize. 

August 14, 2012

Social Media and Change Adoption

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Nisrine Kaderbhay's blog post "Why are Businesses Afraid of Social Media?" argues for the market-facing value of social media as a way to build community with customers and prospects.  Another aspect of the social media debate is the use of social networking tools within the organization. 

According to a recent study by ABI Research, the demand for enterprise social and mobile collaboration will rise dramatically in coming years - reaching a predicted investment of $3.5 billion worldwide by 2016. Integration Developer News quotes the following excerpt from the ADI report: "The primary goal of the social enterprise is to achieve an integrated, coherent, and flexible functioning and development of organizations, making them more agile and capable of facing a highly dynamic business environment.  Many organizations have gone beyond the initial experimentation stage to implement these tools in earnest in order to resolve specific business issues."

For those of us concerned with managing the human and organizational impact of transformational change, this is important news.  The increased volume and speed of change in companies is causing anxiety and confusion across workforces.  Anyone experienced with ERP implementations and other complex transformations knows our clients struggle continuously to ensure adoption of new technology, processes, and roles across the organization.  We needn't belabor the well-known statistics showing that change adoption (or lack thereof) is the #1 factor in the failure of technical implementations.   How can we use social media to address this perennial issue?

Successful change requires three essential conditions:

  1. People know what is expected of them in the new environment.
  2. People have the skills and tools to do what is expected.
  3. People are held accountable for adopting new behaviors.

The above conditions cannot be realized so long as we treat the organization as a mere aggregation of individuals.  There is nothing like the onslaught of change to isolate the individual, and isolation virtually guarantees that the individual:

  1. Misunderstands what is expected.
  2. Fails to learn the necessary skills and tools.
  3. Flees accountability.

Need we mention that isolation also engenders fear?  There is nothing like fear to sabotage change.

If isolation is the enemy of successful change, community is its greatest ally.  Community rescues the individual from fearful isolation and provides the essential social context in which change is more easily adopted.  Productivity-focused social collaboration tools can help build communities that embrace change.  Here are some of the possibilities:

  • Register everyone impacted by the change in an enterprise social network, e.g., Saba People Cloud.
  • Identify change agents and assign them to a community of interest within the social network.
  • Assign individual goals - focused on change adoption - within the change agent community of interest.
  • Create additional stakeholder groups within the social network.  Encourage members of those groups to "follow" the identified change agents, particularly those agents whose activities (visible to their "followers") are of interest to affected stakeholders.
  • Launch, feed, and monitor discussion forums pertaining to change program topics.  Monitoring is important to maintain a positive and constructive attitude to change, yet controversy should not be feared.  Assign a skilled facilitator to manage "hot button" topics and steer the conversation in the right direction.

The above are just a few ideas pertaining to organizational change management.  Enterprise social networking tools can also be used to facilitate informal learning and tacit knowledge sharing, so essential to filling the gaps that are often left by formal classroom training and e-learning.

Have you used enterprise social networking tools to enable change adoption in your organization?  Please leave a comment!  We'd love to hear your story.

 

August 7, 2012

Applying HR Analytics for Competitive Advantage

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One of the key indicators of organizational success is employee performance and HR analytics is an ideal tool for identifying and understanding this relationship. A number of leading firms have embraced this approach, gathering and examining employee management data and applying analytical tools and processes to gain competitive advantage.

For example Google's program known as 'People and Innovation Lab' focuses on employee management best practices in areas such as recruitment, attrition, performance, and their association with organization goals. Similarly, Lockheed Martin has instituted an HR based program that links employee performance with organizational objectives. Best Buy has a similar initiative focused on understanding the correlation between employee engagement and store sales, while Sysco's analytics enabled employee retention program has helped the firm reduce hiring and training costs.

The process for establishing an HR analytics initiative broadly involves the following steps:

i. Establish relationship between employee performance and organization success by formulating and testing various hypotheses and converting them into analytical models.
ii. Deploy and monitor analytical models in organization decision process.
iii. Feedback decision outcome into the analytical system and update the models.

The key to developing a successful HR analytical system is to build it around employees (peer quality, work pressure, growth opportunity, and performance) in alignment with firm's mission.  The idea is to remove guesswork from the decision process by applying high-quality input data. This is enabled by analyzing, confirming and deploying best talent management practices (for example - building models to understand relation between employee background and skill set on performance).

Once deployed and embedded in various organizational processes, such models can support business leaders in making decisions in scenarios such as:

- What would be the impact on financial performance if employee engagement score increases by 1%?
- Based on performance appraisal, which employees are ready to take up leadership role and which would need additional training?
- How does attrition impact project profitability?
- How many employees are required for next two quarters based on plan, industry trends and other socio-economic factors?
- Why and when do employees leave a company and what can be done?
- How to manage employee workload during product launch?

Employee capability, motivation and performance are source of high quality data which managers need to have access to for making better business decisions. Decision making based on employee insights is a model that is difficult to replicate, making it a potential source of competitive advantage and a key lever in attracting and retaining best talent to foster growth.