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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.