Sabado, Setyembre 11, 2010

HOW SMART ARE YOU? by Jolito Ortizo Padilla


Creating Corporate Intelligence will lead to competitive success,but only if you do it properly writes Jolito Ortizo Padilla.

Today's most successful companies are intelligent companies. For example, make a look at Google, Tesco, eBay or Yahoo. These are all companies that use their intelligence to gain competitive advantages and trump the competition. But before a method to create an intelligent company can be determined , we must agree on what we are talking.

Intelligence is an umbrella term that refers to a person's capacity for reasoning, planning, problem solving and learning. It is the very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely about book learning, a narrow academic skill, or taking tests. Rather, it reflects a broader and deeper capability for comprehending our surroundings, making sense of things and figuring out what to do in a situation.

So if intelligence refers to our individual ability to plan, make sense of things, solve problems and learn then these are also the key components of corporate intelligence. Intelligent companies ensure they understand the world in which they operate and use this understanding to inform decision making and learning. They bring together tools such as enterprise performance management, business intelligence, analytics, key performance indicators, management reporting and strategic decision making to generate real competitive advantage.

How to Decide

Intelligent companies lead the way to ensuring that they marshall and analyze available data to add significant value to the organization. They explicitly use the best and most current information to guide decision-making processes.This is much more than just the collection and storage of data and information in large quantities -it is about building knowledge and competitive strategies around data-driven insights.

Decision making needs to be supported by the best data possible in the same way we apply the scientific methods of rigourous data collection and analysis to gain new scientific insights or we expect our doctor to apply the principles of evidenced- based medicine. Intelligent decision making basically means finding the best information you can, facing those facts, and acting on them.

However, while more evidence- based decision making leads to better decisions, the challenge is finding the right data. Thanks to a decade or so of breathtaking advantage in information and communication technologies, we live in a world in which decision makers are bombarded by an ever expanding supply of data , which is placing them and their organization under great strain. The more data that is available , the easier it is to miss the most crucial bits of information.

As data volumes grow at explosive rates, the challenges of managing this information is turning into a losing battle for most companies and they end up drowning in data while thirsting for insights. This is made worse by the severe skills shortage in analytics, data presentation and information communication. This is where quality professionals could play a much more strategic role in the organization, providing some of much needed skills to move organizations forward to become more intelligent. Quality professinals tend to be more data-savvy than other positions and might bring analytical skills such as data analytics, six sigma and value driver analysis to the executive team.

In reality the people who are often seen to be in charge of data and analytics are IT professionals. The reason for this is not that they posessess the best analytical capabilities, but because they have overseen the implementation of software tools-so called business intelligence applications. However, despite huge investments in business intelligence software and solutions in recent years organization are still failing to convert data into strategically valuable knowledge. Business Intelligence alone cannot make this happen-rather it must be used in support of rubost analytic processess. This is huge opportunity for quality professionals to work closely with IT to deliver true business intelligence by joining up analytics and business intelligence efforts with the aim of creating an environment of evidence -based decision making.

The Building Block of Intelligence

There are five key steps to becoming an intelligent company. These steps involve a number of key and best practice skills that top performing organizations need to apply to become more intelligent companies.

. Step 1: Develop more intelligent strategies by identifying strategic
priorities and agreeing your real information needs.
. Step 2: Develop more intelligent strategies by creating relevant and
meaningful performance indicators as well as qualitative
management information linked back to your strategic
information needs.
. Step 3: Develop more intelligent insights by using good evidence to
test and prove ideas and by analyzing the data to gain rubost
and reliable insights.
. Step 4: Develop more inteligent communication by creating informative
and engaging management information packs and dashboards
that provide the essential information packaged in a way that
is targeted and easy to understand.
. Step 5: Develop more intelligent decision making by fostering an
evidence -based culture of turning information into
actionable knowledge and real decisions.

In today's turbulent, unpredictable markets it can prove immensely challenging to identify the core strategic objectives of an organization. But it is only by doing so that it becomes possible to ensure that the analytics we generate are relevant to the organization's competitive positioning and support its greatest information needs.

Step 1
Intelligent organization such as Tesco or Google have created strategic performance management frameworks to guide the collection and analysis of data. Tools such as balanced score-cards and strategy maps can be used to identify high -level objectives. Tesco has clearly routined its strategic priorities in a balanced score card called steering whel.

Once a performance framework is in place, any efforts to use data can be linked back to the strategy of the organization. That way, organizations don't waste valuable time analyzing something that doesn't really matter in the grander scheme of things.

Any efforts to collect and analyze data need to be focused on:
1. The strategic objectives of the organization
2. The big unanswered questions in regards to those objectives.
The executive team in Google for example , has identified the strategic priorities and then formulated a set of questions that they as executives really needed to answer to. These questions are now used to frame the collection of data. Google's CEO Eric Schimdt makes it very clear that any major efforts to collect or analyze data should be linked to strategic questions the excutive team has formulated.

Step 2
Once the data and information needs are clear we can start to collect the appropriate data and performance metrics, that, in turn, will help to answer the strategic questions. The emphasis here is on gathering and organizaing meaningful and relevant data to meet the information needs identified in step one. Organizations need to assess whether the data needed is already held in the organization or devise the best way to collect the data.

A typical trap is to think of data of data as just numbers that come from operational or transactional systems or those that are collected using over-simplified surveys or questionnaires. If organizations want to gain rubost picture of reality then they must keep in mind that data comes in myriad formats-sounds, text, graphics, and pictures are as much data as are numbers.Moreover, there are many methods for collecting data, which can be quantitative (they are concerned with the collection of numerical data) or qualitative (concerned with the collection of non-numerical data). The richest insights seem to come from key performance indicators that are both unique to the organization and observe actual behavior.

Take for example, the insurance firm Progressive Insurance. The company was among the first to make extensive use of consumer credit scores as input to its automobile insurance underwriting. as competitors caught up this technique the company has always found new and innovative ways of collecting and using data to gain a compettive edge. Tesco is another great example. With its Clubcard data it can now observe consumer trends in almost real time and gain insights quicker and in more detail than any of its competitors.

Step 3
Data has to be analyzed and put into context in order to extract information and insights. In the same way as data collection, analysis must support the core strategic objectives of the organization as understood through step one. Central to the analytics process-and in line with the scienific method -is the running of a series of experiments to test assumptions.

Two best practice examples of organizations that make good use of experiments are Yahoo and eBay. Both organizations receive many millons of hits to their homepages each hour. To test new assumptions -in Yahoo's case that making a certain alteration to the homepage will change behaviors of visitors-they randomly assign 100,000,200,000 users to an experimental group and have several million other visitors as a control group. At eBay , simple A/B experiments that compare two versions of a website can be structured within a few days and they typically last at least a week so that they cover full auction periods of selected items.

Step 4
This fourth step focuses on communicating the information and insights extracted in step three. The main focus here is to get the information, in its most appropriate format, to the appropriate decision makers. It is extremely important to keep in mind the target audience and its needs when communicating. Even with proper and tailored analysis it is crucial that the visual presentation tools are clear, infomative and compelling. Information needs to be packaged in different ways in order to help recipients understand the key messages.

Different types of graphs and charts can be used as appropriate, such as pictographs, tally charts, bar graphs, histograms, scatter plots, line graphs and pie charts. Moreover, it is important to use more narratives that provide context and meaning to the data. Using graphs and narrative together will enable the telling of the story, which neither can fully do in isolation.

Step 5
Amassing knowledge, however insightful or compelling, is of little value unless it is turned into action. Decisions have to be made and acted on. However, making this happen often requires a wholesale reworking of the process. This often needs a cultural transformation that might include for example, ensuring that:
. The organization has apassion for learning and improvement
. There is an unswerving leadership buy-in to the principles of being
an intelligent company
. There are eidespread analytical capabilities within the organization
. There is willingness to share information

The five sequential steps of this framework provide a blueprint for any organization that is looking to work more intelligently. However, the logic of good evidence-based decision making is not just linear. an organization needs to ensure that there is a feedback loop between the last and the first step. Once learning has taken place and the decision have been made they in turn inform future information needs.

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