BeyeNETWORK India Blogs BeyeNETWORK India Blogs. Copyright BeyeNETWORK 2005 - 2019 150 31 BeyeNETWORK India Blogs Social Media Analytics Spotlight
Another important focus of Social Media analytics is the emerging measurement of the long tail and the volume of customers that drive the long tail. There are several organizations that have seen the benefit of the long tail; even President Obama saw evidence of this in his fund raising during the elections of 2009.

Crowdsourcing of ideas and opinions have been a trendsetter for B2C companies. This saves the companies a lot of money and provides "agile" techniques to manufacture and sell products or services. Measuring these crowds and their influence provides a baseline for the companies to achieve better in every iteration.

Measuring Social Media will emerge as the key BI trend for this year. Let's watch

]]> Tue, 5 Apr 2011 20:29:37 MST
Thoughts on Data Correctness data validity, data correctness, and some approaches for considering the difference and taking action that I am currently developing at my Data Quality book website. Please check them out!

]]> Fri, 1 Apr 2011 12:56:02 MST
Search vs Sentiment
In order to understand sentiment, you now need to be more focused i.e. narrow your analysis to what people are discussing about the voicing their opinion about the subject of search. If we were to look for Tsumani and Aid, as keywords, in this case your search will return results about organizations providing aid to the victims, any fraud happening in this regard, forums and micro-websites where people are rallying to provide support, their areas of focus and much more. Analyzing this search data and providing an index on the content, the context around the content, the sentiment in the content is the key differentiation of Sentiment as opposed to pure search.

In business speak, sentiment analysis is the expression of a customer or a groups opinion about a product or service that an enterprise is providing. The key business value from understanding Sentiment and beyond include

  • Customer Appreciation
  • Customer Education
  • Customer Connect
  • Brand and Reputation Management
  • True Market Reach and Presence
  • Better ROI from Campaigns and Cross-Sell Opportunities
  • Word of Mouth Marketing
As the world goes more digital, it is essential to be more agile and closer to the customer, this means you need to understand beyond Search and implement Sentiment Analysis and much more.

]]> Tue, 15 Mar 2011 16:15:45 MST
Papers on the Value of Data Quality Improvement I have had a great opportunity to put some thoughts to paper (courtesy of Informatica) regarding methods for understanding business impacts related to data quality improvement and how they can be isolated, organized, measured, and communicated to a variety of stakeholders across the organization.

Here are links for downloading the papers:

Understanding the Financial Value of Data Quality Improvement

Improved Risk Management via Data Quality Improvement

Improving Organization Productivity through Improved Data Quality

Please check them out and let me know your thoughts!

]]> Mon, 14 Mar 2011 06:12:21 MST
Why Analytics cannot be sold as Analytics

What comes to your mind when you hear the word "analytics"?

  • Math algorithms?
  • Operations Research?
  • SAS?
  • Slide and dice?
  • Myterious geeky guys?
  • Direct mailing optimization?

Yet, all of the above does not remotely acknowledge the immense potential of analytics to transform businesses.

I have been speaking in various forums and occasions about analytics. I am truly frustrated. People not only do not get it, but they also think that they get it.

What are the symptons of such ignorance?

  • Thinking of analytics as technology
  • Hiring quantative PhDs assuming that they will crack it
  • Hoping that getting into bed with SAS or SPSS (IBM) will do it
  • "Walk before you run" - first do Cognos or BusinessObjects type reporting, then...

So if you are a CEO, Business unit head, head or marketing, head of risk, head of should go about discovering potential that analytics represents?

The first step - pray - let go of all your notions of analytics. That is a good beginning.

Then, consider the following:

  • Do you know who your most valuable, and potentially valuable customers are? conversely, do you know who your most value-destroying customers are?
  • Does your business process (order acceptance, fulfilment, claims processing, risk assessment, buy-sell decisions, underwriting) have the intelligence to identify and focus on the most value creating transactions and customers, over and above just the judgment of the people attending to the process?
  • Are you pricing your products and service in way not to lose valuable customer AND not to leave money on the table?
  • Is there a way to capture SME intuition used in day to day business, codify, validate and reuse it? That way, every underwriter is as good at your best underwriter.
  • What are your customers and prospects and other constituents saying about you, you reputation, products, service, attitude, after-sales service?

Does this give you some feel of the potential of analytics?

You know, when BI started off, it made some of the above promises. Atleast that is what the sales brochures said. I submit somewhere along the way we got busy with the technology and lost sight of the business capability that it was meant to occasion. BI became too focused on collating, moving, modeling and presenting information with scant regard to what the information indicated. Service providers settled into the comfort of implementing large Datawarehousing programs, often without clearly established business value.

The next time you want to sell analytics - to your clients, stakeholders, boss, CEO - drop the word analytics. Start with the business questions and themes & mention analytics in the end as an enabler.

]]> Sat, 12 Mar 2011 21:26:03 MST
Charlotte NC: Data Quality and MDM Event TOMORROW! It is not too late to sign up for tomorrow's breakfast event in Charlotte, NC: I have been invited by data quality and MDM tool company Ataccama to be the invited guest speaker at a series of breakfast seminar events in early March, with the last one tomorrow. Sign up now!

]]> Wed, 2 Mar 2011 09:29:36 MST
Content and Context
Put a pause to this hullabaloo. What is it that we are trying to convey through the social media channels? is there a context related to what we are sharing and does the context hold the appropriate relevance at the time ot writing and beyond? will all the trackback, commenst and wallposts bear any semblance to that context.

This is where the relevance of context to the content becomes a paramount piece of intelligence in the world of unstructured data. When you want to get intelligence out of the largesse, if you cannot establish context and relevance to the content, it is GIGO (garbage in and out). As you prepare to launch the social media exercise and establish more presence in the digital world, please ensure that content and context are always matched and available.

]]> Mon, 28 Feb 2011 16:28:40 MST
Crowdsourcing Content Management
There are a few "specific" applications that can do this, and they come with built in "content management" and "knowledge management" techniques and workflows. A whitepaper on one "up-start" will be available in about 2 weeks on my channel. If you are interested in specific details or more insights into this process, your comments and feedback are welcome.

]]> Fri, 25 Feb 2011 08:51:05 MST
The Value from Enterprise BI
Sadly in many organizations, there is too much BI i.e too many silos doing the same effort. What this has led to is a state of chaos. Often when a new BI initiative is planned or suggested, the criticism is, "there have been multiple attempts and we failed, what makes you think this time we will be successful".

One of the first things to do is to get your house in an order. This is easier said than done, because each stakeholder wants to own or control their set of data and applications. Another level of complexity arises when each of these silos have their own metadata, taxonomy and business rules. The biggest hurdle often is to get a facilitated governance model to ensure that alignment occurs between the business stakeholders paving the way to a successful implementation of the BI program across the enterprise.

Large programs in organizations with global presence or even within regions of the world have been successful with a strong governance model for the program.

Once you have a governed initiative, measuring the value or ROI of such an investment is quantifiable across layers
  • Unified business metrics and measures
  • Unified business metadata
  • Reduced spend across the enterprise
  • Increased efficiency from data quality
  • Improved data processing cycles, resulting in predictable SLA's
  • Adoption of the BI platform, resulting in single version of truth
  • Trust in data and associated business rules
  • Business will own the data
Most organizations will quantify the ROI or value in terms of hard dollar returns in license cost savings or reduced hardware spend etc. My suggestion is to add the soft benefits to the justification factor for enterprise BI.

This is not a simple topic, this blog post is only a handful of suggestions. More comments or suggestions are welcome

]]> Thu, 24 Feb 2011 18:26:36 MST
The Emergence of Social Analytics
  1. Mobile Devices - The emergence of mobile devices has led to a consumer revolution like never before. Conumers are now able to express opinions on products, services and competition from wherever and whenever, and have become influencer's in their communities of participation
  2. CRM for One Customer - Companies now want to go beyond sentiment analysis, in-fact the theme across CRM space is how to segment and market to one customer i.e. personalized marketing and service offerings
  3. Crowd Intelligence - Companies want to harvest the intelligence of the crowds. There are some stellar success stories - "Ideastrom" and "innocentive"
  4. Software Emergence - Tibco, Visible, Radian6, Nilesen, Webtrends, and much more, there are several software platforms that have emerged to provide the ability to link and gather data from Social Media
  5. Textual ETL and Processing - Inmon's Textual ETL Engine has provided a ground breaking software to parse and process text and get the outputs to be stored into the DW platform. This provides a unique ability to integrate structured and unstructured data.
  6. Hadoop - A strong platform to store BIG data for processing. Provides a easy integration platform
  7. In-Memory Analytics - A new platform to support real time customer behavior and analysis
  8. Visualization - Spotfire, Qlikview and Tableaux have proven to be cool visualization tools for analyzing and reporting unstructured data
  9. Appliances Platform - DW Appliances have found a purpose beyond just being a DW augmentation. The platform from any vendor is built to support BIG data, which is driving the user expectations to support social and unstructured data
In this yesr 2011, we are going to see and hear several success stories from organization on Social Analytics.

]]> Mon, 7 Feb 2011 09:25:03 MST
Meet Me for Breakfast, Data Quality, and MDM - 3 Upcoming Events I have been invited by data quality and MDM tool company Ataccama to be the invited guest speaker at a series of breakfast seminar events in early March at the following locations:

March 1 Bridgewater NJ

March 2 Chicago, IL

March 3 Charlotte, NC

The topic is "Strategic Business Value from your Enterprise Data," and I will be discussing aspects of business value drivers for Data Quality and MDM. I believe that attendees will also get a copy of my book "Master Data Management."

I participated in a few similar events at the end of 2010 and found that some of the attendees posed ssome extremenly interesting challenges, and I hope to share some new insights at these upcoming events!

]]> Thu, 20 Jan 2011 11:24:38 MST
The DW Appliance Still Makes Sense
Whether you accept it or not, the new age of Business Intelligence (driven by increased consumer awareness from both an enterprise and its individual customers perspective) has put demand on Data Warehouses and their ability to execute both in real time and in history; add to this complexity the need driven by analytical queries (now the hunger fueled by In-Memory Analytical Applications), add to this the thought process of Social Media, Unstructured Data and much more to come.

Take this example, you see a customer standing in an Electronics superstore looking at a 3D TV, and then running a barcode scan application on his smartphone to priceshop for the same item from local competetion, and get price and inventory information from competitors across the next 5 miles, you wonder wow where did we land and how did we get here.

This kind of query, analysis and retrieval is what will drive the Data Warehouse of the future. In this case the customer may buy the product here or elsewhere. But the company that wrote the scan application now has potential market research data that it can resell to the stores, manufacturers, distributors and more. In order to make that information consumable, they need a hardware platform and here is where many such organizations will start looking at fast, cheap and commodity platform. A platform to scale, to compute with Hadoop or R and be able to relate to SQL very easily. This is the platform maturity that the data warehouse appliance is maturing to and very quickly.

Most of the vendors today can get you all the bells and whistles, the base idea is which one will fit your bill and your budget. More to come in next posting....

]]> Tue, 11 Jan 2011 22:23:39 MST
Why a database still makes sense for a DW
The question is "do we need SQL" to use a database or can we live in a world of NoSQL. The answer in this case, is probably minimal to no SQL will be the future EDW world. The reason for this being technologies like Hadoop and MapReduce, which are reducing workload complexity from Applications. These applications are being increasingly built and delivered on the cloud and mobile platforms, which require a very light front-end footprint and heavy back-end processing power.

Another driving trend is the increasing adoption to semantic technologies. The semantic technologies propel another trend "in-memory analytics", whereby SQL overheads are minimized on query performance. Backend systems will be SQL intensive and will use a database

A third trend is to integrate "unstructured" or"semi-structured" data and query that result set, which is largely semantic driven.

In conclusion, we will use the DW as a backend, number crunching platform, and slowly move away from "SQL" dependency on the front end, for building out Analytical and BI application.

Virtualization and Cloud will definitely be drivers, but I do not see EDW's or even large DW's being run on pureplay Cloud platforms.

]]> Sat, 25 Dec 2010 15:21:03 MST
Webinar: Fundamental Techniques To Maximize the Value of Your Enterprise Data I will be presenting at a webinar hosted by Talend on December 2 at 2:00PM EDT, 11:00AM PDT on Fundamtental Techniques to Maximize the Value of Your Enterprise Data. In this presentation I will discuss the convergence of the value of three interconnected techniques: master data managemetn, data integration, and data quality. As data repurposing grows, so do the challenges in centralizing semantics, and we wil look at some common challenges. Join me on Dec 2!

]]> Mon, 29 Nov 2010 12:56:03 MST
The Practitioner's Guide to Data Quality Improvement Just published! My new book on data quality improvement, called The Practitioner's Guide to Data Quality Improvement was released a few weeks ago and is now available. The book provides practical information about the business impacts of poor data quality and provides pragmatic suggestions on building your data quality roadmap, assessing data quality, and adapting data quality tools and technology to improve profitability, reduce organizational risk, increase productivity, and enhance overall trust in enterprise data.

I have an accompanying web site for the book at At that site I am posting my ongoing thoughts about data quality (and other topics!) and you can download a free sample chapter on data quality maturity!

Please visit the site, check out the chapter, and let me know your thoughts by email:

]]> Wed, 10 Nov 2010 13:45:15 MST