SEARCHING: Boost a search term

September 25, 2012

BI24 provides the relevance level of matching documents based on the terms found. To boost a term use the caret, “^”, symbol with a boost factor (a number) at the end of the term you are searching. The higher the boost factor, the more relevant the term will be.

Boosting allows you to control the relevance of a document by boosting its term. For example, if you are searching for “Manchester” or “Pie” and you want the term “Manchester” to be returned towards the top of the document list, using the ^ symbol along with the boost factor next to the term. You would type:

This will make documents with the term Pie appear at the top of the document list.

You can also boost grouped terms;


SEARCHING: Grouping Syntax and Queries

September 25, 2012

BI24 supports using parentheses to group clauses to form sub queries. This can be very useful if you want to control the Boolean logic for a query.

To search for either “beef” or “chicken” and “London”, use the query:

This eliminates any confusion and makes sure you that “London” must exist and either term “chicken” or “beef” may exist.

SEARCHING: Fuzzy Searches

September 24, 2012

BI24 enables you to do fuzzy searches by typing the tilde (“~”) symbol at the end of a single word term. For example to search for a term similar in spelling to “hip” use the fuzzy search hip~

This search will find terms like hop and ship.

SEARCHING: Wild Card Searches

September 24, 2012

BI24 supports single and multiple character wild card searches;

? Symbol for a single character wildcard search.

* Symbol for a multiple character wildcard search.

The single character wild card search looks to replace the wild card character with a single character in the search string. For example, if you wanted to find records for postcode sub areas ‘BS1 3EZ’ and ‘BS1 3ET’, you could enter for BS1 3E?

Multiple character wild card searches look to replace the wild card character with zero or more characters in the search string. For example, if you wanted to find records for the branches ‘Falkirk’ and ‘Falmouth’, you could enter for Fal*

The results returned are;


July 9, 2012

A Harvard Business Review Article by Thomas H. Davenport, Article Review by Akhmad Rahadian Hutomo

Since the late of 1990s, the term business intelligence (BI) and its application has been widely known and used in organizations, especially in large enterprises. But in a decade later, they started to realize that changing business environment will needs something more than just BI, which now called business analytics. In 2006, an author named Thomas wrote an article on HBR entitled “Competing on Analytics” which provisions the rising needs for business analytics. Davenport started his explanation on competing analytics by giving some examples on the successful usage of killer apps in some organizations, named Amazon, Harrah’s, Capital One and Boston Red Sox. By utilizing analytics, these organizations are able to knows better about the values that customer want, which inturn be able to squeeze all the value from the processes and make the best out of it. Davenport also point out that, to be an analytics competitor, top-down approach from the senior leadership team, as well as hiring the best people are necessary. Nonetheless, not all organizations are succesfull on using business analytics due to its characteristic. The rest of the articles explains about what organizations can make the best of analytics, as well as the changes that an organization must undergo to adopt it.


Some traditional organizations may not be fully suitable with competing analytics. One best practice that an organization my want to know is how Marriot International using analytics. But, it will not work to some traditional organizations. Davenport’s study found three key attributes that an organization must have:


Analytics competitors do things beyond statistics and spreadsheets. They are using sort of things that could provide them better insights from data, such as:

  • Predictive modelling to identify the most profitable customer.
  • Data warehouse to pool inhous and outside data.
  • Optimized supply chain.
  • Real-time pricing.
  • Sophisticated experiments to calculate impact.

Some analytics competitors, especially inscurance company, like Capital One and Progessive doing series comprehensive experiments to have the best value based on their customers need, even with high-risk.


Successfull analytics competitor will implement analytics using multiple applications in wide busines functions rather than using single app. For some companies such as UPS, Capital One and Barclays Bank are already implementing business intelligence and then shifting towards full-bore analytics competitors. However, Devenport thinks that BI still have some flaws where its still use data which spreads all over the organization. The data may contains errors and make the decision inacurrate, which in contrast, analytics competitors are using centralized function to manage critical data. People within the organization is as important as the technology. Some organization like P&G create a pool of experts from various function to do the analytics.


Changing into an analytics competitor simply changes the organization, and it will require leadership skills to guide the organization towards sucessfull adoption. Its proven that if the initiative just pushed by one-or-two business unit leaders, it will not successfull. There was some key leadership qualities that the article pointed out, such as: appreciation and familiarity with analytics or analytics-minded, intuitive, and have the guts to make decision even not supported by numbers.


Basically Davenport define 4 things that makes an analytics competitor ticks, they are:


Even if an organization have the ability, it is necessary to have certain focus on only a few analytics subjects. Becoming to diffuse can make the organization losing clear sight on the purpose of analytics. Another consideration of focus is about having a deep analysis on at least 7 functions. Nowadays, advanced statistic models and algorithm ca be used widely, including in advertising and other marketing measures. Later on this subtopic, there are examples that sucessfull analytics competitors can’t be done by the organization alone, it also needs to help their vendors and customers.


The right culture to have is the culture to appreciate usage of data, fact and the things between that and the procedure to get it. It also applied in organization with high creativity and intrapreneurship: any innovation should be made based on evidence. However, always justify everything also have payoff: it might be taking long time and costly, so the managers hould balance them in order to make quick decisions.


Analytics competitors hires best people on analytics, bunch of them, to do the analytic-based decisions and make it seamlessly in line with the business. But, the people to do the analytics just as good as how far they can communicate it, so they must have sort of good interpersonal skills. In terms of formula, it might look like this:

Good Analyst = Expertise +Ablity to express it in simple way + Interpersonal skills

Of course, to get people with this quality is not easy, not to mention taking long waiting time. To have an overseas employee might be a good idea.


Analytics and IT are unseparable. It is supported by three pillars: First, THE DATA,whether it is from ERP, CRM, POS, any of them, and a lot of them, means years of data. They put it in data warehouse, which a familiar tools on BI. Second, THE BI SOFTWARE, to collect data from warehouses, analyse them and making reports. And Last, THE COMPUTING HARDWARE which enables a computation power for huge volume of data, quickly.


Well, it might be not long road, as Davenport writhe the articles 5 years before this review written in the late 2011. He was concluding his paper with reminding us that to become an analytics competitor will takes a long time until the ROI, while meantime, it will cost many efforts and expenses. Yet, it can be done gradually from current time by collecting data and refining the system, and equip the organization with analytics-minded people.


Business analytics might be an interestring concept to explore to enrich our current knowledge and view on today’s business intelligence. In contrast with BI, business analytics focuses on gaining insights and overview of organizational performance based on data and statistical methods, supported by BI applications. It also cover the issues of leadership, culture and having a certain quality of analyst within the organization. On the article, Davenport gives the readers a comprehensive look of business analytics without losing the big picture. His writing also well supported with examples which gives personal and easy-to-digest touch on complex concept. A worth to read for BI enthusiasts.

Based on a Harvard Business Review Article Titled “Competing on Analytics” by Thomas H. Davenport Published on January 2006, Article Review By Akhmad Rahadian Hutomo for Business Intelligence Assignment, Information System, Faculty of Computer Science, Universitas Indonesia on October 2011.

The right BI tools for the job

July 2, 2012

I get the following question very often. What are the best practices for creating an enterprise reporting policy as to when to use what reporting tool/application?

Alas, as with everything else in business intelligence, the answer is not that easy. The old days of developers versus power users versus casual users are gone. The world is way more complex these days. In order to create such a policy, you need to consider the following dimensions:

Report/analysis type

  • Historical (what happened)
  • Operational (what is happening now)
  • Analytical (why did it happen)
  • Predictive (what might happen)
  • Prescriptive (what should I do about it)
  • Exploratory (what’s out there that I don’t know about)

Interaction types

  • Looking at static report output only
  • Lightly interacting with canned reports (sorting, filtering)
  • Fully interacting with canned reports (pivoting, drilling)
  • Assembling existing report, visualizations, and metrics into customized dashboards
  • Full report authoring capabilities

User types

  • Internal
  • External (customers, partners)

Data latency

Report latency, as in need the report:

  • Now
  • Tomorrow
  • In a few days
  • In a few weeks

Decision types

  • Strategic (a few complex decisions/reports per month)
  • Tactical (many less-complex decisions/reports per month)
  • Operational (many complex/simple decisions/reports per day)

Data sources

  • In an ideal situation (a single EDW, a single BI platform), this would not be relevant, but in most real situations, it is.

Self-produced versus IT-produced based on criteria such as:

  • Report complexity (number of joins, etc.)
  • Resulting report set size
  • Mission criticality of the report
  • External exposure
  • Level of operational risk
  • Individually used versus workgroup shared versus shared across department, LOB, enterprise

You will then need to come up with an 8-dimensional (or more) matrix (good luck 🙂 ), where at each intersection you need to indicate first and second choice for a specific BI tool/app best fit to address each use case. Did I miss any dimensions? Also, when and if you come up with something like this, or even as you are experimenting with prototypes, I’d love to see them and comment.

This blog originally appeared at Forrester Research.

Can you predict your business future….

May 30, 2012

Turning data into actionable information is challenging. Your company has different departments with different needs therefore requiring different reports, from different, and often multiple, data sources.  Decisions need to be made quickly; therefore, lengthy waits are unacceptable.

Your business information should be transparent and predictable. It is essential for organisations to understand the steps involved in reaching predictability.

Steps to predictability

So what step are you on? What do you need to move up to the next step?

Having the right tools to understand your business can save resources, time and money. You can empower users with self-service access to the latest data they need. These tools can be used to create trends and perform ‘what if’ analysis allowing your company to be proactive rather than reactive.

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