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: Range Searches

September 24, 2012

Range searches allow you to match documents whose field(s) values are between the lower and upper value specified by the range search. Range searches can be inclusive or exclusive of the upper and lower bounds. Sorting is done alphabetically.

Using square brackets performs inclusive range searches:

This will find documents whose “Branch Name” field has values between “Bristol” and “Chester” inclusive.

Using curly brackets performs exclusive range searches:

This will find all documents whose “description” fields have values between “Bristol” and “Chester”, but not including “Bristol” and “Chester”.


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.


Interesting findings by Columbia Business School and NYAMA study in 2012

August 1, 2012

Sample findings:

• 91% of senior corporate marketers believe that successful brands use customer data to drive marketing decisions

• Yet, 39% say their own company’s data is collected too infrequently or not real-time enough

• And 51% say that a lack of sharing customer data within their own organization is a barrier to effectively measuring their marketing ROI

• Large firms are much less likely to collect new forms of digital data like mobile data (19%), than they are to collect traditional customer survey data such as on demographics (74%) and attitude (54%)

• 85% of large corporations are now using social network accounts (e.g. brand accounts on Facebook, Twitter, Google+, Foursquare) as a marketing tool

• 65% of marketers said that comparing the effectiveness of marketing across different digital media is “a major challenge” for their business

• 37% of respondents did not include any mention of financial outcomes when asked to define what “marketing ROI” meant for their own organization

• 57% are not basing their marketing budgets on any ROI analysis

• 22% are using brand awareness as their sole measure to evaluate their marketing spend

In order to leverage the opportunities of big data, marketers need to improve their ability to:

• Collect meaningful customer data from a variety of sources, including real-time data

• Link that data to metrics developed for measuring marketing ROI

• Share data across the organization, linking datasets together at the customer level

• Utilize this shared data to effectively target and personalize marketing efforts to customers

In order to effectively harness the capabilities of new digital tools, marketers need to:

• Set clear business objectives for any digital marketing effort

• Develop a variety of metrics for new digital tools—from audience metrics, to engagement metrics, to financial metrics

• Develop models that link channel-specific digital metrics (like retweets or Facebook interactions) to universal metrics, including your key performance indicators (KPIs)

• Continuously innovate new measurement models, as new digital tools and marketing rapidly evolve

Above all, get started. Start with the basics of determining marketing ROI so you will create the largest impact on your organization:

• Make sure you’re using some kind of metrics on most of your marketing.

• Be ready to invest in getting some kind of data relevant to your measures.

• Make coordinating your traditional and digital media campaigns a goal.

• Set specific measurable objectives for all your campaigns.

• Put ROI in stated objectives for all your vendors (so they know your expectations to retain them or to cut them loose).

• Link marketing ROI to employee compensation, perhaps a bonus.

• Start today… as the pay off and learning curve will likely take a few years.

Then, move on to ROI best practices:

• Make sure your marketing metrics are accepted by finance.

• Make sure your data is: timely, actionable, linked at the customer-level, used to personalize marketing and target customers.

• Share your data across your organization.

Thanks to


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/


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.


Big Data and the race for real-time: things are about to get interesting

May 30, 2012

Life is about to become much more interesting in Big Data land. While data have ever been with us, it’s only recently that Hadoop and other Big Data technologies have dramatically altered the economics of collecting, managing, and utilizing data to drive businesses. Now it appears that even these open technologies, from Hadoop to Cassandra, are about to be transformed themselves, leaving the world of batch-oriented data processing behind in favor of real-time analytics.

Hold on to your seats.

As powerful as Hadoop is, it has one significant shortcoming: it’s batch-oriented. Even a few years ago, this was fine, as just being able to gather and crunch the data after the fact, at a much lower cost than traditional data mining, was a huge win. But, as Todd Papaioannou, formerly vice president of cloud architecture at Yahoo! and now founder of Continuuity, argues, “people are expecting much more of a real-time experience” on the web, something that Hadoop hasn’t historically delivered. He further notes,

It’s not clear to me, as an industry, that we have nailed that [real-time analysis] problem. It is clear to me that we need to solve that problem, and that the next big wave of applications is going to be real-time and to get to real-time, you have to take the human out of the loop.

This isn’t to suggest anyone should stay on the sidelines and wait for Hadoop (and other NoSQL databases) to achieve real-time status. Far from it. Many an industry is already transforming itself through the Big Data intelligence that Hadoop and other technologies enable, batch orientation and all.

After all, just starting to work with data at all is a big deal. The stakes are huge, as a wide variety of industries are sprinting to take advantage of the treasure troves of data available to them. As the Wall Street Journal reports, it used to be enough to mine receipts and other consumer data to find nuggets of information that could affect your business. But now the Holy Grail is “getting and making effective use of information as it happens.”

We’re not far off. Just as the financial services industry used to operate on a 20-minute time lag with stock information, but now streams real-time stock information to traders and others, so, too, will industries as varied as retail and agriculture increasingly base decisions on up-to-the-second information about purchasing trends, weather patterns, and more.

Of course, we still need better Big Data-savvy applications to make sense of the data. But these are coming.

What is needed now, perhaps more than anything else, is to enable Hadoop, the clear front-runner in the NoSQL sweepstakes, as a real-time data storage and processing tool. Continuuity doesn’t indicate how it plans to accomplish this, but there are others working on this same problem, including Nodeable. (Not surprisingly, we believe we have cracked the code. :-) Regardless of who crosses that real-time finishing line first, many industries are benefiting from the Big Data gold rush today, and will benefit even more when we can make Hadoop real-time and enable a host of data-intensive applications to tame some of Hadoop’s complexity.

Again, the stakes are huge, which is why so much investment is going into this, both in terms of venture capital and enterprise IT. As these converge, expect to see a transformation of how businesses operate. In real-time. At high levels of efficiency.

Thanks: http://blog.nodeable.com/page/2/


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