“Big Data” and “Small Returns” are two labels you won’t find together in marketing literature all that often. But the reality is that’s exactly what many companies are finding as they venture onto the pastures of computing’s third platform.

The role of technology companies -- when they're not innovating -- is to make their clients’ journeys less painful and more valuable. We’re spotlighting three very different companies, Solix, Nara Logics and Actian, that seem to be rising to that challenge.

Has Nara Logics Scored the Golden Trifecta of Personalization?

Amazon guru Werner Vogels recently said that the reason recommendation engines sometimes get you wrong is because they don’t know enough about you. Well that might be the case with me because I recently bought Keurig Decaf K-Cups from Amazon and now they’re sending me emails offering deals on power drills. No thanks! I don’t even know where my screw driver is.

If the biggest, brainiest recommendation engine can get it so wrong, do other businesses even stand a chance?

It’s not the lack of information but taking advantage of the wealth of available information that’s key, at least that’s what the MIT pedigreed neuroscientists at Nara Logics think. And while we’ve all had an earful on big data, we haven’t heard nearly enough about how personalization that’s accessible to the non-Netflix’s of this world actually works.

That may now change.

“We’ve discovered the golden trifecta of personalization -- a brain-like, breakthrough algorithm; efficient ways to mine extremely large data sets; and a continuously updating, deep learning system,” says Thomas Copeman, Nara Logics’ founder and CEO.

The problem with personalization systems, it seems, has been a focus on processing power (the more of your data we can get our hands on and crunch, the better we will get to know you) instead of brainpower.

In other words, personalization technology has been more artificial than intelligent, as the company’s website says.

What Nara does differently is that it looks for the millions of features and attributes (and attributes of attributes) that make up every person, place and thing that’s relevant to a customer’s database and then augments it with additional information, reviews and opinions. They pool all of this data into a single neural network and uncover preexisting relationships and build both expected and unexpected connections between them. They use this to uncover an individual’s Digital DNA™

That DNA is then applied to match an individual’s unique preferences with a client’s products and services in real time. The end result? Personalized content recommendations and messaging that makes true one-to-one segmentation possible.

How difficult is it to get going? It’s a SaaS solution, it learns while it works, and the company says that its tools make it easy for any business to tune and manage the results and metrics that personalization produces.

And, oh, by the way, if you’re not a megacorp, now worries, Nara says it’s looking to work with organizations of all sizes.

Archived Data No Longer a Drag, Thanks to Solix

Up until recently Information Archiving didn’t yield many returns, unless you count keeping innocent CEOs out of jail or putting those that are guilty behind bars.

Enterprises are forced to keep certain inactive data readily accessible to comply with regulations and in case they are sued. The thinking was (and sometimes still is) that data should be dumped as soon as legally possible for two reasons: first, if you store huge volumes of old, inactive data with active data inside an application, your application performance slows down. And second, local data storage, when there are huge volumes of data continuously being created, is expensive. Storing data off premises and outside of the application is arduous because data must be extracted, transformed and loaded (ETL) out of the application and back, if an audit were to occur.

This being the case, many enterprises have kept decommissioned applications on standby and operating environments running so that information could be accessed on demand, just in case.

Recent innovations have created a new option that not only makes this less of a problem, but also frees data trapped in silos so that it can become an asset rather than just a liability. We explained how this works in an article about EMC’s Info Archive earlier this year.

The one thing that hasn’t been addressed much until now is how huge (big data sized) volumes of archivable data, which is getting bigger by the second, can be stored nearby while remaining in compliance via Information Lifecycle Management (ILM) and be easily searchable.

Solix, a leading provider of Enterprise Data Management (EDM) solutions, today announces its new Solix Big Data Suite, a next-generation Enterprise Archiving and Enterprise Data Lake application platform for Apache Hadoop, to address the problem.

The company says that its Big Data Suite provides an ILM framework to govern enterprise data and analytics applications and Apache Hadoop as a nearline repository to store less frequently accessed data. The idea is that by moving less frequently accessed data to a nearline repository, production application performance improves, infrastructure costs are reduced and big data analytics become possible.

The solution could literally unearth gold from what looks like a pile of garbage. The Big Data Suite can perform analytics at petabyte scale.

Solix’s Big Data Suite consists of a data lake that provides a copy of production data and stores it “as is” in Hadoop File System (HDFS) as highly scalable, low cost, bulk storage. The COPY process eliminates the need for ETL processing during ingestion. Once it’s in the HDFS, enterprise data may be better described or transformed later for use with business analytics applications.

The suite also provides a nearline repository for archiving and retirement of enterprise applications, which improves production application performance because there’s not as much data for them to crunch. It also helps to reduce infrastructure costs and helps meet compliance goals. Online enterprise data is first moved and then purged from its source location according to ILM policies for governance, risk and compliance.

Needless to say, the solution adheres to data archiving best practices in that MOVE and PURGE processes are coordinated and validated. Ditto for data governance since enterprise data is ingested and stored based on retention policies and business rules. Archive data is classified for security and compliance requirements such as legal hold, and universal access to all data is provided through structured queries, reports and full text search for business objects.

The beauty of Solix’s solutions and its Solix Big Data Suite in particular, is that they transform the data game from being one of drudgery to one of possibility and value.

Actian Introduces Big Data 2.0 to Accelerate Path to Value

You don’t have to look far to find an organization that says it’s not reaping the rewards that big data promised.

It’s not the data’s fault and Apache Hadoop, and the folks who work with it, have come a long way. The problem it seems is knowing where you want to go, and then defining a clear path to get there.

That’s precisely what Actian wants to help customers do. Yesterday they introduced their Big Data 2.0 Clear Path Program.

Developed by data scientists in Actian’s Analytics Center of Excellence, the program provides a complete roadmap to address specific business challenges, with proven methodologies and pre-defined workflows to accelerate customers along their analytics journeys.

According to the company, the program provides pre-built workflows that customers can use out of the box with their own data; workshops with Actian data scientists to apply the methodology to each customer’s unique problems and datasets; and blueprint diagrams, available at no cost via the Actian website, laying out big data-centric advanced analytics methodology to accelerate time to value, deepen data discovery and blend traditional data with new data types for more accurate results.

The first set of unveiled blueprints concentrate on Customer Analytics to help businesses better understand their customers to drive more revenue from individual customers and increase market share.

They include:

  • Customer Profile Analysis: Create a 360-degree view of customer
  • Micro-Segmentation: Create more granular customer segments, down to a “segment of one”
  • Customer Lifetime Value Analysis: Discover and cultivate high-value customers
  • Next Best Action: Predict and influence customer decisions
  • Campaign Optimization: Rapidly build and refine campaigns
  • Churn Analysis: Prevent high-value customers from leaving
  • Market Basket Analysis: Uncover hidden profitability

For companies that are struggling to glean actionable insights from their data stores, or for those that are interested in big data analytics but don’t know where to begin, Actian may provide a valuable resource.