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;


COMPETING ON ANALYTICS: AN ARTICLE REVIEW

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.

ANATOMY OF AN ANALYTICS COMPETITOR: MUST-HAVE CHARACTERISTICS FOR ORGANIZATIONS

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:

WIDESPREAD USE OF MODELLING AND OPTIMIZATION

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.

AN ENTERPRISE APPROACH

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.

SENIOR EXCECUTIVE ADVOCATES

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.

THEIR SOURCES OF STRENGTH: WHAT MAKES AN ANALYTICS COMPETITOR RUNS

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

THE RIGHT FOCUS: HAVING A CLEAR SIGHT

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 JUSTIFY EVERYTHING QUICKLY

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.

THE RIGHT PEOPLE: THE BEST OF THEM

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.

THE RIGHT TECHNOLOGY: THREE PILLARS

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.

THE (LONG) ROAD AHEAD

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.

COMMENTARY

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.

http://ianhutomo.wordpress.com/2012/07/07/business-intelligence-in-human-capital-driven-companies/


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.


Brand Over Substance?

July 4, 2011

I come from an age when the Spectrum was the new kid on the block. I remember the smell of the machine when I took it out its box. The comforting heat omitted from it when I switched it on and sat at it writing code for hours on end.

For those of you who don’t know, the Spectrum was a competitor to the BBC computer, Commodore 64 and later the Acorn. Despite it’s rubber keys and the need to be some form of contortionist to get the keyboard to type “Beep” or “Poke” I loved it.

I bought magazines about the latest games. I brought them to school and ended up arguing with fellow geeks about the merits of the Spectrum over the Commodore 64. Why there wasn’t enough RAM in a VIC 20, why the Oric (which made me think of the talking plastic cube Orac out of Blake’s 7) came too late and was a year too late and never going to take off.

After learning to code and writing a few games I realised that I couldn’t do things by myself anymore. Games were becoming a big business. In fact games were no longer something that could be knocked up by a spotty teenager in their bedroom upstairs. Games now required teams of graphics designers, sound engineers, testers, marketing moguls and hordes of developers.

The games Industry was starting to boom. Hardware suppliers started to build low cost, powerful gaming machines. It was GAMES WAR. It was Atari against the PC, it was the Amiga against the Atari, it was both of them against the Sega and then all three against the Nintendo. That was the start and it’s been relentless ever since.

Think of some of the technology battles that have occurred over the past 20 years;

  • Windows versus Apple
  • DOS versus OS2
  • VHS versus BETAMAX
  • IE versus NetScape
  • iPod versus Walkman
  • Sky versus BSB
  • HD DVD versus Blue Ray
  • Sega versus Nintendo
  • Play Station versus XBOX
  • Google versus MSN
  • Sky versus Virgin Media
  • iPhone versus Blackberry
  • iPhone versus Android

All of these have been great products but there is only ever one winner. Is it always the best product? What is it that makes the winner “float to the top?” Is it the quality of the product or is it the way the product is marketed and branded?

As a consumer what do I want? Do I want the product that everyone else has or do I want the best product? Personally I want the best product. I don’t go for designer labels and I don’t go for things that are in vogue, I go for the things that make a difference to me, make a difference to my business and above all helps the bottom line.

I run a software company that writes bleeding edge technology. This is my story about why we do what we do and why I think that what we have is all about substance. For us the brand comes second however if we get that right too then perhaps someone will write about us in the future and see us as “remember the way things changed in BI? Remember Bi24 versus Business Objects, remember Bi24 versus Microsoft?

When I started my life as an IT professional, I was an analyst programmer specialising in COBOL and IDMSX on ICL mainframes. It was not long before I got into Oracle and enjoyed the delights of SQL, Oracle Forms 2.3 and Report Writer version 1.0 (which was shocking…)

I then moved onto SQL Windows, Ingres, Informix, SQL Server, DB2, C, C++ and finally Java with a bit of .NET before I decided enough was enough – get other people to do the programming as keeping up–to–date with new technologies and standards is a young mans game!

Throughout this time, I concentrated on data access and management information solutions. Designing applications to put data into a database and finding ways of getting it back out again – what a fulfilling career!

All sounds simple, in fact it really is however despite your best efforts you can only do as good a job as the tools that you are using will allow.

If the database does not support stored procedures or functions you end up writing more code and things tend to go slower. If you need to program in a language that doesn’t have very good de–bugging facilities you end up having to take twice as long and resort to adding loads of print statements and stack traces to help you identify where you are in the code when things go wrong – back in the 80’s, the Spectrum had no de–bugger so I was in my element here!

Up until the last 10 years of my career I had no say in what software languages we used or what database technologies we adopted. They were all chosen for different reasons. Some because they were functionally great but the majority because they had a good brand and the guy making the decision felt a “warm glow” when they spoke to the supplier (as if things didn’t work out they could always defend their position by the fact the supplier was in the magic quadrant at Gartner or had a balance sheet that RBS or Northern Rock would have died for).

Throughout this time I battled to create the best software solutions possible based on the raw materials I had at my disposal. I was pretty successful although if these solutions were re–produced today using the latest and greatest, the users would have been blown away.

One of my greatest loves was SQL (Structured Query Language) which I learned on a series of excellent Oracle courses in the late 80’s. A lot of what I programmed subsequently was around accessing databases, and I became pretty good at getting the best out of Oracle and applying what I knew to other databases “to get data out” that either I or someone else had managed to “get the data in”.

I grew to be (and would like to think still am) a SQL expert who could write line after line of SQL to navigate around the most complex of database schemas to return the relevant data rows back to the screen or a report. Twenty years of experience meant that SQL was no trouble to me.

Unfortunately, in later years, implementing solutions to customers, I found that end users and other IT departments did not share the same depth of knowledge that I have in “getting data out of a database” (perhaps this is because they didn’t know how to “get the data in?”) and struggled to use the same tools I grew to love as a serious software developer.

I now realise that getting data out of databases is not easy. It requires a lot of expertise in understanding where the data is, how to get at the data and how to bring it back in a meaningful form. Let a user loose on a database who is not a “professor in databases” is like letting a tourist attempt to translate a foreign paper when they don’t understand the language. They may understand part of it but will never see the full picture.

5 years ago I got all excited about search technology. It wasn’t new but I was starting to use it more and more to find “stuff” on the web and was amazed how easy it was to get results and how quick it was to get answers to the most bizarre of questions. The fact the answers were somewhere amongst several million search results was irrelevant to start with, I was simply amazed at the scale and the speed of this new technology.

It was not long before I formed a few ideas in my head about how searching for stuff using a search engine was child’s play and how searching for stuff in a database was for the “weird and unwashed” – like myself.

“What would happen if we used search technology to search databases?”

“How great would it be if people that understood the business but didn’t understand SQL could finally get at their data?”

From this idea came CXAIR – A business intelligence tool that takes the best elements of search engine technology and combines them with the best elements of business intelligence. A master stroke!

It’s super-fast, easy to use, easy to implement, based on the latest technology, an evolutionary leap from other business technology that sits on top of the now legacy database systems however… we’re not Microsoft. We’re not Oracle. We’re not Business Objects.

I run a UK company with a team of extremely gifted geeks who like me love software and love “getting data out” of systems – they’re not to bothered about “getting data in” as we leave this to the database specialists because after all databases are designed for “storing data” not “retrieving data”.

We understand business intelligence but don’t have the legacy the big boys have and have been able to come up with something that is fundamentally different from any other BI tool. It’s easy to use! It’s lightning fast! It’s installed and working in under a day!

We are all about substance. What we have is real and not over egged by some giant marketing machine. What we have is amazing. Give us a call and we’ll show you what we mean.

Richard Lewis


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