Change management tools for speedy implementation

In this part of the writing on change management. we cover  three important tools that are in use

Employee Reward Recognition

Enterprise Risk Management

Big data analytic

Changing behavior through Employee Reward and Recognition Schemes

Employee Reward and recognition programs continue to be flogged by HR community and also the  management of  organizations to manipulate the performance or specific behavior of target group. Rewards are founded on the hypotheses  that positive reinforcement can lead to reinforcement of  existing behavior and  repetition of  behavior.

 It is also true when incentives are introduced to change behavior in certain areas like education, contributions to public good, conflict arises between direct extrinsic effect of the incentives and intrinsic motivations in the short and the long run. Do rewards deliver results? The answer depends on what we mean by “results .” There are research evidences to suggest that, rewards succeed at bringing   temporary change but when it comes to ensuring lasting change in attitudes and behavior, neither rewards, nor punishment, are effective 

Once the reward is withdrawn or loses shine , people revert back to their status quo behaviors. Rewards which act as extrinsic motivators are unable to alter the attitudes that underlie human behaviors.

In one study when employees were asked about their top concern in their current job and organization pay did not come as top item but as fourth item. On the contrary when they found out about their organization’s effort in changing things which would protect their job, make it very challenging and safer they were motivated to stay and perform. 

A considerable and growing body of evidence suggests that the effectiveness of rewards depend on the design, the form and how they affect intrinsic motivations and social motivations, and after effect of withdrawals.

Change and Enterprise Risk Management-ERM

Managing risks in enterprise has become necessity when one looks at the highly unpredictable risks that come out of external triggers. Risk management  is about establishing sound methods and processes that can be used to manage situations triggered by unforeseen events and to minimize or eliminate adverse impacts on capital or revenue. The method is assessment in terms of probability of occurrence and by estimating the magnitude of impact, deciding a response strategy, and monitoring progress. By identifying and proactively addressing risks and opportunities, business enterprises are able to protect and insulate from catastrophic consequences. The extent of unpreparedness is evident when one looks at the Lehman’s risk exposure. Lehman Brothers had more than 35 percent of its net tangible equity in three commercial real estate investments. Very few had idea about the concentration in so few investments, and the extent of high risk and reckless endeavors with the finance of investors with no ERM in place. It is surprising how the most brilliant financial brains failed to provide for risks making it appear as if that was deliberate oversight. ­­Change management becomes a lot easier with ERM in place

ERM may also include creating strategy maps. The Strategy map is one of the most important tools developed by Kaplan and Norton as part of their Balanced Scorecard approach. It is a way of distilling strategy into a collection of objectives and showing the causal relationships between objectives.

The Strategy map is a tool for explaining and demonstrating how intangible assets, such as people, information systems, culture, processes etc., create customer outcomes and ultimately deliver tangible financial benefits for shareholders. A well-constructed Strategy map is the summary of the organization’s 'strategic story' - a narrative which clearly explains what the organization is seeking to achieve and how they will go about achieving their strategic steps.

Change management and Big Data Analytics-

The explosive growth of data can be gauged when we see that every day 2.5 quintillion bytes of data are being created .and that explosive growth is not over years but in the last two years. This data comes from every source: satellites are used to gather climate information, people post every minute in social media sites digital pictures and videos, online purchase transactions, and mobiles phones GPS signals to name a few. 

This data is now labeled as big data; big data is a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software tools Big data analytics is the process of examining large data sets containing a variety of data types -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.

 Big data analytics enables organizations to gain valuable business insights.as never before. Companies are bringing together traditional Performance measurement approaches with innovative analytics approaches to create performance analytics that drive fact-based decision-making and generate truly powerful competitive advantages. Initial focus was on collecting and reporting a small number of balanced KPIs to manage and monitor the strategy execution in companies, the latest evolution of BPM leverage the ever- increasing amounts of data. 

We now have access to and combine innovative tools for financial management, with predictive analytics, Business Intelligence, big data analytics and interactive performance reports streamed to our mobile devices with up-to-date data visualizations. Today organizations aiming to achieve top-performance have to rely on integrated strategic and operational performance insights, generated from traditional KPIs as well as from an analysis of data generated from social media and mobile. This enables usage of the insights to predict future performance as well as identify, evaluate new opportunities and risks. 

Business performance management today involves data consolidation from various sources, querying, and analysis combined with forecasting with insights Big Data analytics helps companies to generate real competitive advantages by making their performance management more agile and relevant. The overall amount of data and analytics is growing in every industry and data science is deployed to extract non trivial information. It’s mission-critical to determine how to acquire new customers, do more cross-selling and predict demand and failures. Normal business intelligence and descriptive analytics, and even traditional software data bases is unable to handle those situations. BI is a techniques used in Extraction transformation and loading, and analyzing business data. 

Objectives of BI implementations include (1) insight into a firm's internal and external strengths and weaknesses, (2) deeper understanding of the relationship between different data and making informed decision, (3) detection of opportunities for change and transformation (4) optimizing cost and deployment of resources.BI platforms provide a range of capabilities for building analytical applications.  Oracle OBIEE, SAP Business Objects are the most popular ones. There are many choices and combinations of BI platforms, capabilities and use cases as well as many emerging BI technologies such as in memory analytics, interactive visualization and BI integrated search.

Advanced analytics has capability beyond any human capability in managing large volumes of data, and dealing with highly complex settings. Digital businesses are adopting data science methods in reducing storage costs. The advanced analytics isn’t another complex form of analytics.  Descriptive analytics is used in reporting what has happened. Another way to describe descriptive data is as "content without context" i.e. facts that describe a particular phenomenon, without meaning or relevance; whereas advanced analytics is used using predictive and prescriptive techniques to solve problems. Predictive analytics predicts future outcomes or customer behavior such as a customer’s shopping behavior or a machine’s failure. 

Prescriptive analytics goes further, suggesting actions to take based on the predictions. For example, the concept of preventive maintenance has been revolutionized with the use of predictive analytics thereby preventing unscheduled and costly downtime. The technologies for advanced analytics are different from those for normal decretive analytics, and require different skills and these skills typically include statistics, machine learning and operations research

Sentiment analytic as change driver:

Data on public opinions and sentiments are no longer generated by sponsored surveys but they are out in the web in social media for anyone to harness...They have major influence on change management strategies as they can be the trigger for the changes in the product are design or marketing. The study of sentiment analysis is also known as opinion mining and retrieval Sentiment analysis refers to the use of natural language

processing, text analysis and computational linguistics to identify and extract subjective information from different type of sources. In this age of explosive Web and mobile Technologies, millions of consumers choose to express their opinions on a wide range of topics on the web in blogs, product/service reviews. For example, twitter generates everyday 8TB of data.

Sentiment analysis is the process that “aims to determine the attitude of a speaker or a writer with respect to some topic. companies are now actively checking the tone of email messages and other communications. KIA cars have traditionally been associated known for fuel efficiency and value for money, but the makers want KIA cars also to be known for "great design and cool technology”. Their biggest challenge for KIA brand is changing consumer perception and ensuring emotional connection. 

To change the brand image, Kia uses a tool that can swiftly analyze large numbers of opinions on the Web, including blogs, the micro blogging site Twitter and social networking service Facebook. It's called Mass Opinion Business Intelligence which can deliver continuously, real-time feed of relevant consumer sentiment, gathered from millions of sites.

The emerging technologies can now tell the decision makers on hourly basis about people’s sentiment on a particular product or service, current and ex-employees’ feeling about the company and its executive leadership, stock behavior, advertising campaign.

If a company can measure and monitor customer sentiment faster, the change to be done with goal to market success becomes easy. Automated Sentiment Analysis refers to the computerized processing of text in order to determine the sentiments, the attitudes, thoughts, and judgments, of the people expressing those sentiments. 

Unfortunately, a number of pitfalls confound the accurate analysis of the sentiments that are conveyed by online statements. Automated sentiment analysis is the process of designing systems of computer to identify sentiment within content through Natural Language Processing (NLP). Various sentiment measurement platforms employ different techniques and statistical methodologies to evaluate sentiment across the web. Some rely 100% on automated sentiment, some employ humans to analyze sentiment, and some use a hybrid system. 

Automated Sentiment is Meaningful when dealing with large Volume to provide some directional insight and set the tone for further analysis. Managements use sentiment data which is a highly useful metric when combined with other data because context is important, to form conclusions in devising change strategy.  For example, employees when they express using terms like “bad culture, “low pay, and “won’t recommend anyone “, in Glass door, it is time to think of taking some change decision for the management. 

Almost every company institute’s annual planning and sets aggressive revenue and margin goals only to see it slipping most of the time quarter by quarter. When the goals and objectives are not met as desperate measure changes are initiated to close these gaps between expectations and actual. Technology to support BPM activities has evolved massively over the past few years. Initially the focus was on storing and reporting performance information using databases and dashboard solutions.

More mature approaches then allowed by companies to create closed loop systems that help to integrate operational and strategic performance data and align traditional performance measurement with analytics. This allows companies to analyze the data and integrate performance reporting with, for example, financial management tools or other tools such as risk management or project management. 

Today’s solutions do everything, combining integrated BPM platforms with the ability to perform predictive and big data analytics, along with root-cause analysis on past and future data, empowering companies to visualize performance in interactive graphs and reports delivered to mobile devices