Photo by Meredith Cook at Breckenridge, CO on a “blue bird” day, a clear day following a fresh snowfall.  It’s unrelated to SaaS or Cloud (or is it?); just nice to look at.

SaaS and cloud are starting to be used interchangeably (“we’re looking for a cloud solution”) but they really are not the same thing.

Software-as-a-Service (SaaS) is: software that is made available for use based on access to features, time, number of transactions, number of users, or a combination of variables.  A ‘cloud’ is simply a server – a computer you don’t own or maintain – that sits somewhere other than in your building, that you access to run applications or store data.

SaaS describes a type of software, cloud describes a type of platform.

So you can see it’s possible to take applications that you own, and put them in ‘the cloud,’ and also possible to use software you don’t own, but pay for based on usage, that is sitting in your data center with all your other applications.

But there are more important distinctions.

Type of Software-as-a-Service: Is it software that only you access (single tenant), or is it an application that many other people or companies use (multi-tenant)?  Multi-tenant is generally lower cost, but with less specialized functions for your particular enterprise.  Is it truly SaaS, or just a full cost, configured-for-you application hosted by someone else whose costs have been spread out monthly over 5 years to look like a SaaS solution?  True SaaS works like a subscription: sign up, pay by month and use it; when you no longer need it, you cancel.

Type of Cloud: Is it a private or public cloud, or a hybrid?  A private cloud is a single tenant environment (your enterprise) where you control access and have firewalls for security and where you can define the hardware.  A public cloud is where you are paying for a part of an existing cloud server environment; here, you “rent” space and the advantage is flexibility, low cost, and the ability to scale capacity up or down based on your needs.  Hybrid clouds offer both private and public spaces for you to use.

Let’s look at examples; in both cases we will use the scenario of a manufacturing company selling to major retailers in the U.S. and Canada.

True SaaS:  You contract with a company that offers tools for analytics (software) together with point-of-sale (POS) data for your products for your largest retail customers.  You pay by month to access and analyze POS data. The costs vary depending on how many report levels you want to see.  You access the application via internet, anyone in your company can use the service, and you can cancel at any time.

True Cloud: For purposes of experimenting with business intelligence applications, you purchase database space from a vendor, at a cost that varies depending on how much space you use.  You can scale up or down in terms of the storage you need.  You can connect to this space with a variety of tools for transporting  data and you can install and remove applications easily.  For running your business day to day, you can host your most critical applications in this cloud, and have in reserve an identical cloud with servers ready to take over in cases of disaster or over-capacity of your main servers.

Before you accept at face value the terms ‘cloud’ or ‘SaaS,’ make sure you understand what the vendor is telling you. Ask for details and explanations.  What the vendor thinks is cloud or SaaS can certainly be different from what you expect.

Image by lofrev.net

It’s getting harder to determine which software vendors have what capabilities. This is because:

• The number of technology startups has increased;
• Big software companies have been acquiring other firms to increase the breadth of their capabilities;
• Established firms are rapidly making changes to their suite of applications – adding capabilities so quickly that it’s difficult to land on a static evaluation and comparison vs. other vendors.

The branding of specific functionality continues to proliferate. Firms don’t define their software’s features all the same way – they give them a brand name, which only adds terminology that is unnecessary and gets in the way of a clear comparison of features.

Firms are offering products and services that overlap what other firms offer, making it more difficult to weed out who truly offers what you want.

It used to be that, if your company needed software in some form – packaged or custom – it was “installed” on a server. Then a “client” for the software – a relatively small piece of software – was installed on desktops so that the software on the server could communicate with the user on the desktop.

In between the two was a local-area network (LAN), which is jargon for a wired connection. In this configuration, a user could launch the client software on a PC, and the client would, via communication over the LAN to the server, enable the user to fully use all the features of the software.

Players in this market looked like this:

• The firm that wrote the software;
• The firms or independent consultants that support the software;
• The firm(s) that helped you to install, configure, test and launch the software you bought;
• The company from which you bought your servers;
• The company that supplied your LAN and wide-area network (WA)

All of this has changed. Now there are vendors that can do all of the above, without stepping inside your building, through a web portal.

How do you get what you need in this environment?

1. Find the software vendors that know your industry and understand what they offer. Software companies are usually organized according to what they call industry verticals, such as health care, pharmaceuticals, consumer goods, banking. A company with lots of clients in your industry is a good start
2. Find the software vendors that are the best for your targeted functional area such as sales, manufacturing, finance, etc.
3. Focus on the firms that are well represented in #1 and #2 above

Image by Marcos Gasparutti, CC license

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.

Another kind of Saas: The village of Saas-Fee and surrounding mountains, in southwestern Switzerland. Photo by Robbie Shade, CC license.

Everyone has a SaaS version of the software your business is considering, right?  Vendors know that terminology like SaaS and cloud trigger generally positive reactions from potential customers — having these options implies the vendor is up to date with the latest advances in software platforms.

Never mind what the vendor calls it.  What you need to understand right from the beginning is: who owns the software, where is it hosted, and how is it priced?

1. True SaaS is software that you do not own and do not maintain.  Some vendors will promise to maintain their software and to support your users, but the application nonetheless is installed on one of your servers.  This is not true SaaS.
2. An application that is customized just for your business, hosted by the software vendor, and whose costs are just spread out over a period of years via monthly payments, is not true SaaS.
3. True SaaS is priced like a subscription, one that is, say, monthly, and that you can cancel at any time.
4. True SaaS will have little or no startup costs.
5. A SaaS application can be what is called “single-tenant” or “multi-tenant.”  The former means you are using an application that is configured just for your enterprise; the latter means you are using the same base code or a copy of the same base code that many other firms are also using.  The former will cost more.

Why would companies say their software is SaaS when it really isn’t?  Because the term SaaS has a halo effect — it implies the solution is advanced, quickly implemented, efficient, and inexpensive.  A vendor can even make the pricing look like Software-as-a-Service (#2 above).

Know the details before you waste too much time.

Related: A Software Vendor Checklist

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.

If you visit OmPrompt in the UK you can stay in the nearby dreamy, historic, and beautiful town of Oxford. Photo by Tejvan Pettinger, CC license.

What if you want to make sure all of the data on a customer’s order is correct before it is processed in your systems?  Or if you want to stop orders that you can’t fill based on time constraints?  Many firms at first apply humans to these tasks (customer service), but what if you process 55,000 orders per year?  There must be some way to automate this screening process, right?

There are at least three ways to handle this problem with software.

The first is to modify your ERP system, to incorporate the rules you will apply to every order that is received.  Not only will the ERP system apply the rules and identify exceptions but alert you to those orders that must be stopped and changed before proceeding through the order cycle.

This is certainly a viable option, but you’ll find yourself pouring lots of money into it, with no end in sight, because as soon as you successfully incorporate a set of order checks, the business will come up with a new set of checks based on a new logistics or pricing strategy.  Most ERP systems are not built with user-configurable parameters; changes require programming by expensive resources.

The second is to build some type of middleware filter.  This is a custom application that every order would pass through before entering your ERP system.  The middleware would catch and hold up non-compliant orders.  A lot of firms adopt this route, because it is so much less expensive and much more flexible that tearing into the guts of an expensive ERP system.  And there is no shortage of eager programmers, within company IT departments, who want to build such a tool, in the language they know best on the computing platform they know best.

This second option is better than the first, but barely.  The second option makes you dependent on the genius that builds and maintains that middleware tool, and that is almost as bad as being dependent on an ABAP (SAP code) programmer.  And there will be instances where no matter how brilliant your middleware architect, certain business requirements will be beyond the capabilities of him/her and/or the software they have created.

The third option is to outsource your order capture, partially or entirely, to a firm that has the systems agile enough to handle the ever changing order filters your business may want to apply.  The advantage of these firms is that they serve as adapters: the incoming order is converted to a standard computer script where all of your rules can be applied; the script is then converted to the digital code (such as EDI) needed by your ERP system.

One firm that does this is UK-based OmPrompt, which specializes in automating business processes for large firms all around the world.  Their services are based on transaction volume and are very inexpensive relative to software modifications or building your own system.

I favor outsourcing the job not so much because someone else can do it better (although that is true) but because you cannot predict what is coming down the road in terms of new business requirements.  You need an open system, an infinitely-adjustable capability, if that is possible.  And firms that make their living on being adaptable to requirements like yours will always have more value to offer.