Image by David Bleasdale, CC license

Analytics is one of the top buzzwords in business software today. Analytics software is often marketed as a tool for business intelligence, data mining or insights. It’s the crystal ball software: tell me things I don’t already know, and show me ah-hahs or other exciting revelations that, if acted on, will increase sales, cut costs or produce some other benefit.

The essential elements for analytics are:

1) A design for your ‘stack’ which is just a term for layers: usually at the bottom you have a data layer, then a translation layer, then on top of that some kind of user interface layer. The translation and user interface layers are usually provided by the analytics vendor; you provide a place for data storage.

2) A way to send the data to your data storage, automatically, which is usually referred to as “ETL” or extract, transform, and load. SnapLogic and Informatica are two vendors who offer these tools.

3) Some way to “harmonize” the data, which means define each data element and how it will be used in analytics. “Sales” will mean such and such, “Gross Margin” will be defined as ……

These three components can be on-premise in your building or in a cloud hosted by a vendor.

SAS, based in North Carolina, has long pioneered this space, and now many business software firms claim to provide “robust analytics.” The problem: what constitutes “analytics”? Canned reports are not analytics. So you’ll need to shop this category knowing that probably the most serious applications will come from firms that are dedicated to analytics.

International Data Corporation (IDC) reports that the business analytics software market is projected to grow at a 9.8% annual rate through 2016. IDC describes the market as dominated by giants Oracle, SAP and IBM, with SAS, Teradata, Informatica and Microstrategy rounding out the top 10 in terms of sales revenue. Although the top 10 account for 70% of the market, IDC reports that “there is a large and competitive market that represents the remaining 30%…hundreds of ISVs (Independent Software Vendors) worldwide operate in the 12 segments of the business analytics market…some provide a single tool or application, others offer software that spans multiple market segments.”

Here are some other interesting analytics or business intelligence (BI) products: Qliktech provides easy-to-develop dashboards with graphical representations as well as tabular and exportable reports. Its Qlikview software is an “in-memory” application, which means that it stores data from multiple sources in RAM, allowing the user to see multiple views of the data, filtered and sorted according to different criteria.

Information Builders (IB) is a software company classified by advisory firm Gartner as a leader in BI applications. IB’s main application, WebFocus, is a flexible, user-friendly tool that is popular with sales teams because salespeople use it while visiting customers to enhance their selling messages with facts and visual interpretations of data.

WebFocus has a “natural language” search capability, making it useful to monitor and analyze social media.
Birst, named by Gartner as a challenger in the BI space, is a cloud-based (SaaS) application that offers “self-service BI,” deployment to mobile devices, adaptive connectors to many different types of data sources, in-memory analytics, drill-down capabilities, and data visualization. The Birst tool also has a data management layer, allowing users to link data, create relationships and indexes, and load data into a data store.  Tableau is another similar vendor.

It’s useful to start small and experiment with analytics.  People in your organization with good quantitative skills and imagination can experiment with tools, usually at very low cost.  Soon you will see some interesting results and will want to do more…but make sure to put in place some rules about what constitutes sanctioned and official “analytics” in your organization, to prevent uncontrolled proliferation of un-validated information.

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Finance and accounting functions were among the first to be automated through software. The sheer volume of numbers and calculations, reporting requirements, tax filings and payroll mechanics, plus the fact that nearly every business has to engage in these activities, made the area perfect for software.

When just these basic functions are needed, not much distinguishes one finance application from another. They all post transactions to a cost center and sub ledger account, they all capture sales and costs and calculate required P&L and balance sheet data, and they all provide reports. They might distinguish themselves in terms of ease of use or report writing, or banking account integration, or cash management, or some other aspect.

Many finance applications are simply bookkeeping systems; if you want real analysis you’ll need to extract data to Excel, Business Objects, or another analysis and reporting tool. My own experience with both Oracle and SAP bears this out: even these leading finance packages are mostly concerned with accounting and financial, not management reporting.

Oracle and SAP both have what they call “business intelligence” capabilities, but they are contained in separate modules that must be purchased and integrated with the core software. So companies can easily spend millions implementing SAP or Oracle, and still find themselves extracting data into Excel spreadsheets for basic business analysis.

My experience is that most finance applications lack budgeting and financial modeling capabilities. It is one thing to know that your prior month results were over budget because of rising fuel prices, and quite another to project the future profit impact of different oil price scenarios. At what point would it make sense to switch to alternative fuels, to pass on some of these increased costs, or to buy oil futures as a hedge? A typical finance application won’t help you to answer these questions because they mostly record and categorize costs based on what already happened, not what might happen in the future.

Yes, there are “what if” modeling applications available on the market, but as a stand-alone application they aren’t very useful, since you have to enter all of your data, as if you’re using an Excel spreadsheet. The modeling application needs integration with your ERP to be most effective. Your ERP is the source of all kinds of data needed for financial modeling: production costs, formulas, material costs, transportation costs, revenue by product, as well as cost standards and budget information. This data changes frequently based on business conditions, competition, labor costs, and many other factors.

Microstrategy, Oracle Hyperion and Cognos are leading names in the financial modeling and analytics areas, but other, smaller firms are emerging. Netsuite, the ERP-in-the-cloud vendor, offers an add-on financial modeling application. Netsuite’s web site states that the modeling application features these capabilities:
• Dynamic formulas and assumptions
• “Actuals” data incorporated into new forecasts
• Workflow management
• Planning of full financial statements
• Unlimited versions for “what-if” analysis
• Multi-dimensional models for complex sales and product planning
• Multiple currency budgeting
• Graphic drag-and-drop report builder
• Multi-version variance reporting (vs. budget, vs. plan, vs. forecast)

A3 Solutions is another, smaller firm offering financial modeling applications, either on-premise or as Software-as-a-Service. A3 uses the Excel spreadsheet as the user interface, claiming it is the friendliest environment for creating what-if scenarios, and provides tools to link multiple sources of corporate data and manage modeling versions dynamically and virtually through its Spreadsheet Automation Server. A3 claims McDonalds, Honda, Toyota, T. Rowe Price, and American Airlines as clients. Simplicity, speed of implementation, and low cost are A3’s main selling points.

Once you have the “system of record” stabilized in a strong finance application, as well as good controls over product, customer, and sales data, you can start to think about these higher-level analytical tools. Define a standard model for delivering analytics, put someone in charge of the data, and tightly control the “official” analyses that are produced.

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In their recently published book, “Big Data: A Revolution That Will Transform How We Live, Work, and Think,” Viktor Mayer-Schonberger and Kenneth Cukier say that big data will provide a lot of information that can be used to establish correlations, not necessarily precise cause and effect.

But that might be good enough to extract the value you need from big data.

Three examples from their book:

1. Walmart discovered a sales spike in Pop-Tarts if storms were in the forecast. The correlation was also true of flashlights, but selling more flashlights made sense; selling more Pop-Tarts didn’t.
2. Doctors in Canada now prevent fevers in premature infants because of a link between a period when the baby’s vital signs are unusually stable, and, 24 hours later, a severe fever.
3. Credit scores can be used to predict which people need to be reminded to take a prescription medicine.

Why did the people involved in the above examples compare such different sets of data? One possible reason: because they could – relatively quickly and at low cost – this was made possible by superfast data processing and cheap memory. If you could mash together all kinds of data in large volumes – and do so relatively cheaply – why wouldn’t you until you found some correlations that looked interesting?

You can begin experimenting – a process I endorse — with Big Data. You need three basic components:

1. A way to get the data, whether out of your transaction systems or from external sources, and into a database.
2. Superfast data processing (a database with enormous amounts of RAM and massively parallel processing). This can be had on a software-as-service basis from Amazon and other vendors.
3. Analytics tools that present the data in the visual form you want. Vendors include Oracle, Teradata, Tableau, Information Builders, Qlikview, Hyperion, and many others.

Correlations are usually easier to spot visually. And visualization is where the market seems to be going, at least in terms of hype and vendor offerings. New insights are always welcome, so we shall see what sells and what doesn’t.

The assessment from Gartner seems about right to me at this point in time: that big data is both 1) currently in the phase they call the “trough of disillusionment;” and 2) promising enough that its use in BI will grow sharply.

Image: visualization of data showing places in New York City frequented by tourists (red) and locals (blue); by Eric Fisher; Creative Commons license.

Another emerging segment of the analytics software market is data virtualization (DV), referred to by some as Information-as-a-Service (IaaS), which enables access to multiple data sources, usually in real time, without the time and expense of traditional data warehousing and data extraction methods.

Forrester Research defines DV as solutions that “provide a virtualized data services layer that integrates data from heterogeneous data sources and content in real-time, near-real-time or batch as needed to support a wide range of applications and processes.”  Data Visualization, on the other hand, refers to methods of displaying data in a highly visual way, with the purpose of finding a display mechanism that reveals more insight than traditional reporting methods (see ‘What is Data Visualization’?)

Traditional BI or analytics methods rely on some form of data warehousing, in which pieces of data are extracted, usually from transaction systems, transformed or “normalized” (i.e., “formatted”), and stored in tables according to some type of schema. “Customer Account Number,” for example, may belong in the “Customer” table, and so on. As covered in the book, building a data warehouse and getting it to work right can take years, and require substantial technical skills that even many mid-sized to large companies just don’t have.

Data Virtualization aims to overcome this disadvantage by not extracting data from their original sources but by viewing and manipulating the data inside the DV tool or layer to build your analysis.  In simple terms, a DV tool is supposed to let you “see” sources of data in different applications and databases, and to “select” data from those sources for your queries or analysis.

While it’s feasible to connect directly to external applications and other data sources, whoever owns or manages that application or data source may prevent you from connecting directly, for security reasons, or to avoid overloading the database, to avoid corrupting the data, or simply because the data is proprietary and the provider allows access only through an environment external to the data source.  These are some of the barriers I have encountered.

Forrester estimates an $8 billion market for DV software.  Forrester notes that the current market is dominated by big companies such as SAP, Oracle, Informatica, Microsoft and Red Hat, and specialized firms like Composite Software, Denodo Technologies and Radiant Market.

Experimenting on a small scale is a good idea here.  Vendors are willing to show you capabilities and do small pilots to prove the concept you might be considering the software for.

Image: “Data Center.” by Stan Wlechers, CC license

So what is Big Data, particularly Big Data analytics? Why all the hype?

Big Data is what it implies: tons of data. We’re talking millions or billions of rows here – way too much for standard query tools accessing data on a disk.

What would constitute “tons” of data? Every bottle of “spring,” “purified” or “mineral” water that was scanned at a grocery store checkout during the month of July 2011; the brand, the price, the size, the name and location of the store, and the day of the week it was bought. That’s six pieces of data, multiplied by the estimated 3.3 billion bottles of water sold monthly in the United States.

Big Data analytics is the process of extracting meaning from all that data.

The analysis of big data is made possible by two developments:

1) The continuation of Moore’s law; that is, computer speed and memory have multiplied exponentially. This has enabled the processing of huge amounts of data without retrieving that data from disk storage; and

2) “Distributed” computing structures such as Hadoop have made it possible for the processing of large amounts of data to be done on multiple servers at once.

The hype you read about Big Data may be justified. Big data does have potential and should not be ignored. With the right software, a virtual picture of the data can be painted with more detail than ever before. Think of it as a photograph, illustration or sketch – with every additional line of clarification or sharpening of detail, the picture comes more into focus.

Michael Malone, writing in The Wall Street Journal, says that some really big things might be possible with big data:

“It could mean capturing every step in the path of every shopper in a store over the course of a year, or monitoring every vital sign of a patient every second for the course of his illness….Big data offers measuring precision in science, business, medicine and almost every other sector never before possible.”

But should your enterprise pursue Big Data analytics? It may already have. If your company processes millions of transactions or has millions of customers, you have a lot of data to begin with.

You need three things to enable Big Data analytics:

1. A way to get the data, whether out of your transaction systems or from external sources, and into a database. Typically this is done with ETL or Extract, Transform, and Load software tools such as Informatica. Jobs are set up and the data is pulled every hour, day, etc., put into a file and either pushed or pulled into a storage environment.
2. Superfast data processing. Today, an in-memory database (a database with enormous amounts of RAM and massively parallel processing) can be acquired and used on a software-as-service basis from Amazon Web Services at a very reasonable cost.
3. User interface analytics tools that present the data in the visual form you prefer. Vendors include Oracle, Teradata, Tableau, Information Builders, Qlikview, Hyperion, and many others. The market here is moving toward data visualization via low-cost, software-as-a-service tools that allow you to aggregate disparate sources of data (internal and external systems, social media, and public sources like weather and demographic statistics.