Enrich Customer Insights With Unstructured Data

Over the past several years, Forrester's research has written extensively about the age of the customer. Forrester believes that only the enterprises that are obsessed with winning, serving, and retaining customers will thrive in this highly competitive, customer-centric economy. But in order to get a full view of customer behavior, sentiment, emotion, and intentions, Information Management professionals must help enterprises leverage all the data at their disposal, not just structured, but also unstructured. Alas, that's still an elusive goal, as most enterprises leverage only 40% of structured data and 31% of unstructured data for business and customer insights and decision-making.

So what do you need to do to start enriching your customer insights with unstructured data ? First, get your yext analysis terminology straight. For Information Management pros, the process of text mining and text analytics should not be a black box, where unstructured text goes in and structured information comes out. But today, there is a lot of market confusion on the terminology and process of text analytics. The market, both vendors and users, often uses the terms text mining and text analytics interchangeably; Forrester makes a distinction and recommends that Information Management pros working on text mining/text analytics initiatives adopt the following terminology:

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What To Do When A CIO Pushes Back On Your Agile BI Platform?

 
CIO pushback is part of a typical growing pain of all business intelligence (BI) startups. It means your land and expand strategy is working. Once you start expanding beyond a single department CIOs will notice. As a general rule, the earlier the CIO is brought on board, the better. CIOs who feel left out are likely to raise more objections than those who are involved in the early stages. A number of BI vendors that started out with a strategy of purposely avoiding the CIO found over time that they had to change their strategies - ultimately, there’s no way round the CIO. Forrester has also noticed that the more a vendor gets the reputation of “going round” the CIO, the greater the resistance is from CIOs once they do get involved. 
 
There is of course also the situation where the business side doesn’t want the CIO involved, sometimes for very good reason. That notwithstanding, if there’s a dependency on the CIO when it comes to sign-off, Forrester would strongly recommend encouraging the business to bring him/her to the table. 
 
The two key aspects to bear in mind in this context are:
 
  • CIOs look for transparency. Have architecture diagrams to hand out, be prepared to explain your solution in as much technical detail as required, and have answers ready regarding the enterprise IT capabilities listed below.  
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It's 10 O'Clock! Do You Know If Your BI Supports Actual Verifiable Facts?

Delivering broad access to data and analytics to a diverse base of users is an intimidating task, yet it is an essential foundation to becoming an insights-driven organization. To win and keep customers in an increasingly competitive world, firms need to take advantage of the huge swaths of data available and put it into the hands of more users. To do this, business intelligence (BI) pros must evolve disjointed and convoluted data and analytics practices into well-orchestrated systems of insight that deliver actionable information. But implementing digital insights is just the first step with these systems — and few hit the bull's eye the first time. Continuously learning from previous insights and their results makes future efforts more efficient and effective. This is a key capability for the next-generation BI, what Forrester calls systems of insight.

"It's 10 o'clock! Do you know if your insights support actual verifiable facts?" This is a real challenge, as measuring report and dashboard effectiveness today involves mostly discipline and processes, not technology. For example, if a data mining analysis predicted a certain number of fraudulent transactions, do you have the discipline and processes to go back and verify whether the prediction came true? Or if a metrics dashboard was flashing red, telling you that inventory levels were too low for the current business environment, and the signal caused you to order more widgets, do you verify if this was a good or a bad decision? Did you make or lose money on the extra inventory you ordered? Organizations are still struggling with this ultimate measure of BI effectiveness. Only 8% of Forrester clients report robust capabilities for such continuous improvement, and 39% report just a few basic capabilities.

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Continuing Trend Of Data Exploration - Tableau Closes One Of The Remaining Gaps In Its Portfolio, Acquires HyPer

One of the reasons for only a portion of enterprise and external (about a third of structured and a quarter of unstructured -) data being available for insights is a restrictive architecture of SQL databases. In SQL databases data and metadata (data models, aka schemas) are tightly bound and inseparable (aka early binding, schema on write). Changing the model often requires at best just rebuilding an index or an aggregate, at worst - reloading entire columns and tables. Therefore many analysts start their work from data sets based on these tightly bound models, where DBAs and data architects have already built business requirements (that may be outdated or incomplete)  into the models. Thus the data delivered to the end-users already contains inherent biases, which are opaque to the user and can  strongly influence their analysis. As part of the natural evolution of Business Intelligence (BI) platforms data exploration now addresses this challenge. How? BI pros can now take advantage of ALL raw data available in their enterprises by:
 
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Get ready for BI/Analytics vendor landscape reshuffle

When I read articles like today's WSJ article on mutual funds exiting high tech startups and triangulate the content with Forrester client interactions over the last 12 to 18 months (and some rumors) I am now becoming convinced that there will be some Business Intelligence (BI) and analytics vendor shake ups in 2016. Even though according to our research enterprises are still only leveraging 20%-40% of their entire universe of data for insights and decisions, and 50%-80% of all BI/analytics apps are still done in spreadsheets, the market is over saturated with vendors. Just take a look at the 50+ vendors we track in our BI Vendor Landscape. IMHO we are nearing a saturating point where the buy side of the market cannot sustain so many sellers. Indeed we are already seeing a trend where large enterprises, which a couple of years ago had 10+ different BI platforms, today usually only deploy somewhere between 3 and 5. And, in case you missed it, we already saw what is surely to be a much bigger trend of BI/analytics M&A - SAP acquiring mobile BI vendor Roambi. Start hedging your BI vendor bets!

Four Rules To Build Successful Business Intelligence Business Cases

Rule #1. Don't just jump into creating a hefty enterprise wide Business Intelligence (BI)

Business intelligence and its next iteration, systems of insight (SOI), have moved to the top of BI pros' agendas for enterprise software adoption. Investment in BI tools and applications can have a number of drivers, both external (such as regulatory requirements or technology obsolescence) and internal (such as the desire to improve processes or speed up decision-making). However, putting together a BI business case is not always a straightforward process. Before embarking on a BI business case endeavor, consider that:

  • You may not actually need a business case. Determining whether a BI business case is necessary includes three main considerations. Is it an investment that the organization must make to stay in business, should consider because other investments are changing the organization's IT landscape, or wants to make because of expected business benefits?
  • A business sponsor does not obviate the need for a business case. It may be tempting to conclude that you can skip making a business case for BI whenever there is a strong push for investment from the business side, in particular when budget holders are prepared to commit money. Resist this impulse whenever possible: The resulting project will likely suffer from a lack of focus, and recriminations are likely to follow sooner or later.
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What Qualifies A BI Vendor As A Native Hadoop BI Platform?

With the incredible popularity of big data and Hadoop every Business Intelligence (BI) vendor wants to also be known as a "BI on Hadoop" vendor. But what they really can do is limited to a) querying HDFS data organized in HIVE tables using HiveQL or b) ingest any flat file into memory and analyze the data there. Basically, to most of the BI vendors Hadoop is just another data source. Let's now see what qualifies a BI vendor as a "Native Hadoop BI Platform". If we assume that all BI platforms have to have data extraction/integration, persistence, analytics and visualization layers, then "Native Hadoop/Spark BI Platforms" should be able to (ok, yes, I just had to add Spark)

 

  • Use Hadoop/Spark as the primary processing platform for MOST of the aforementioned functionality. The only exception is visualization layer which is not what Hadoop/Spark do.
  • Use distributed processing frameworks natively, such as
    • Generation of MapReduce and/or Spark jobs
    • Management of distributed processing framework jobs by YARN, etc
    • Note, generating Hive or SparkSQL queries does not qualify
  • Do declarative work in the product’s main user interface interpreted and executed on Hadoop/Spark directly. Not via a "pass through" mode.
  • Natively support Apache Sentry and Apache Ranger security
 
Did I miss anything?

What are the typical Text Analytics jobs and responsibilities?

Hi

I am kicking off a research stream which will result in the "Text Analytics Roles & Responsibilities" doc. Before I finalize an RFI to our clients to see who/how/when/where they employ for these projects and applications, I'd like to explore what the actual roles and responsibilities are. So far we've come up with the following roles and their respective responsibilities
  • Business owner. The ultimate recipient of text analytics process results. So far I have
    • Brand manager
    • Customer intelligence analyst
    • Customer service/call center analyst
    • Risk manager
    • Competitive intelligence analyst
    • Product R&D analyst
    • Anyone else?
  • Linguist/Data Scientist. Builds language and statistical rules for text mining (or modifies these from an off-the-shelf-product). Works with business owners to
    • Create "golden copies" of documents/content which will be used as base for text analytics
    • Works with data stewards and business ownes to define corporate taxonomies and lexicon
  • Data Steward. Owns corporate lexicon and taxonomies
  • Architect. Owns big data strategy and architecture (include data hubs, data warehouses, BI, etc) where unstructured data is one of the components
  • Developer/integrator. Develops custom built text analytics apps or embeds text analytics functionality into other applications (ERP, CRM, BI, etc)
  • Others?
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Selecting Professional Service Provider For Your Business Intelligence/Information Management/Analytics/Big Data Projects

You've done all the right things by following your enterprise vendor selection methodology. You created an RFI and sent it out to all of the vendors on your "approved" list. You then filtered out the responses based on your requirements, and sent out a detailed RFP. You created a detailed scoring methodology, reviewed the proposals, listened to the in-person presentations, and filtered out everyone but the top respondents. But you still ended up with more than one. What do you do?

If you shortlisted two or more market leaders (see Forrester's latest evaluation)  I would not agonize over who has better methodologies, reference architectures, training, project execution and risk management, etc. They all have top of the line capabilities in all of the above. Rather, I'd concentrate on the following specifics
 
People
  • The vendor who proposed more specific named individuals to the project, and you reviewed and liked their resumes, gets an edge over a vendor who only proposed general roles to be staffed at the time of the project kick off.
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Build More Effective Data Visualizations

Industry-renowned data visualization expert Edward Tufte once said: "The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?" He's right: There's too much information out there for knowledge workers to effectively analyze — be they hands-on analysts, data scientists, or senior execs. More often than not, traditional tabular reports fail to paint the whole picture or, even worse, lead you to the wrong conclusion. AD&D pros should be aware that data visualization can help for a variety of reasons:
  • Visual information is more powerful than any other type of sensory input. Dr. John Medina asserts that vision trumps all other senses when it comes to processing information; we are incredible at remembering pictures. Pictures are also more efficient than text alone because our brain considers each word to be a very small picture and thus takes more time to process text. When we hear a piece of information, we remember 10% of it three days later; if we add a picture, we remember 65% of it. There are multiple explanations for these phenomena, including the fact that 80% to 90% of information received by the brain comes through the eyes, and about half of your brain function is dedicated directly or indirectly to processing vision.
  • We can't see patterns in numbers alone . . . Simply seeing numbers on a grid doesn't always give us the whole story — and it can even lead us to draw the wrong conclusion. Anscombe's quartet demonstrates this effectively; four groups of seemingly similar x/y coordinates reveal very different patterns when represented in a graph.
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