Tag Archives: Big Data

Building Solid Foundations in Big Data & Analytics

23 Aug

Originally Published on the DATUM, LLC Site: Building Solid Foundations in a data Swamp


Much has been written about Big Data, Data Science and Artificial Intelligence and how these will change the world through the insights being derived from the data. This especially applies to the unstructured data. A recent article in the Harvard Business Review indicated that “cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all.”[1]

There are a few challenges however:

  1. How do users create understanding and ensure they have the correct data for their needs if it has no structure?
  2. How do you create a single logical view of data in a big data world, where things are not only highly variable, but also are often widely disbursed.
  3. How do you address analytical requirements, where the notion of data quality and how it is managed, varies significantly?
  4. How do you expose the data lake(s) to users in a form that is discoverable, understandable and useable?

This blog is the first in a series to explore the data management and governance perspectives related to these four challenges.

Challenge #1: Unstructured Data

The question of how to deal with unstructured data consistently raises its head as a challenge for organizations. First let’s get a few things out there:

  • There is no such thing as truly unstructured data. There is always a structure of some sort.
  • Knowing what you have and having the right tools are foundational capabilities.
  • The degree of structure required for data to be useful is variable and context driven.

Let’s take these in order:

Creating Structure

Structure is created in one of two ways:

  • Through reorganizing data so that it has structure
  • Through labeling data

The former is what happens to data in a traditional data environment as it is moved through the ecosystem – from Source to Enterprise Data Warehouse for example. The latter is what happens in a big data environment. The data is never moved, but rather labels are added to it to provide the ability analyze that data.

Note: Data can be labeled incrementally. Newly acquired data, can only be labelled with the acquisition date, the source, and the file type. As data moves through the data lifecycle, it will be “curated” to add additional context.

A little labelling goes along way!

How much the data needs to be labelled to be useful can be viewed on a continuum. At one end simply knowing that you are looking at emails provides enough information to know how to organize them; while at the other end, social media sentiment analysis will require extensive labelling. Regardless, the right tools are required to provide logical structure to the unstructured data.

When it comes to tools that cater to unstructured data one key capability is entity tagging or entity extraction tools that can recognize an entity and tag it with a label that makes sense to the organization – essentially tag it with the approved glossary term. Entities can be:

  • Anything from a simple named list such as a “product”; or
  • Extremely complex and map entities into semantic ontologies such as a “JV” is a “Joint Venture”, which is a type of “Company”, which is an “organization” that has “owners”.

Complementing the tagging capability is a flexible indexing capability. Tools like Elastic Search allow users to search based on the structures discovered in the data.  For example, a “Joint Venture “is a type of company. Additionally, these tools can create an index to allow discovery of similarities in text.

The key point is that once data is organized, users and applications can begin to apply big data techniques to expose insights:

  • How do emails cluster on a timeline?
  • Are organizations mentioned in the text? (Could be Joint Ventures, Partnerships, LLCs, PLCs, and so on.)
  • Is there a change in frequency over time? Related to what entity types / categories?
What does this mean from a data management perspective?

From a data management perspective unstructured data will require some new capabilities. However, in some respects, it really is more of the same: What data do I have and where is it?  Is my data labelled to communicate understanding?  Is my data easy to acquire and apply in my context?

If you think of tags or labels as descriptive metadata, and the list of tags and labels as reference metadata, then you can place this activity into the traditional data management context. In order for data to be discovered, understood and integrated across systems and use cases, organizations need to:

  • Have a disciplined approach to how data is described and labelled. This starts with creating a set of glossary terms that can be linked to define meaning. [2]
  • Implement the governance framework that ensures the data is aligned to – and remains aligned to – the business understanding of what the data is, and how it is used.

Organizations often do not face this challenge until they need to manage data across the various operational silos, geographic regions or functional domains. The ned to understand product lifecycle data with regional focus group data is an example of a cross functional/geography/silo data mash up that delivers highly impactful insights.

Be sure to check back in as we address the next three challenges!

References

[1] Harvard Business Review What’s Your Data Strategy? Leandro DalleMule, Thomas H. Davenport; May –June 2017 Issue https://hbr.org/2017/05/whats-your-data-strategy

[2] With reference to linking of data, the simple link types are “subset of”, “superset of”, “same as”. (See SKOS for a deeper discussion on knowledge organization). For example, using this approach one can tag pharmaceutical products to identify synonyms as recognized by the ISO standards; and synonyms of the same product that are commercial names. This is the challenge faced by organizations implementing the IDMP standards.

[3] For a good case study of data integration across disparate data sets using SKOS metadata see Healthcare Research Information

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Forensic Analytics and the search for “robust” solutions

12 Jan

Happy New Year!

This entry has been sitting in my “to publish” file for some time. There is much more to be said on the topic. however, in the interest of getting it out … enjoy!

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This entry was prompted by the article in the INFORMS ANALYTICS Magazine article titled Forensic Analytics: Adapting to a Growing Pandemic by Priti Ravi who is a senior manager with Mu Sigma and specializes “in providing analytics-driven advisory services to some of the largest retail, pharmaceutical and technology clients spread across the United States.”

Ms. Ravi writes a good article that left me hanging. Her conclusion was that the industry lacks access to sophisticated and intelligent monitoring equipment, and there exists a need for a “robust fraud management systems” that “offer a collective set of techniques” to implement a “complex adaptive approach.” I could not agree more. However, where are these systems? Perhaps even what are these systems?

Adaptive Approaches

To the last question first. What is a Complex Adaptive Approach? If you Google the phrase, the initial entries involve biology and ecosystems. However, wikipedia’s definition encompasses medicine, business and economics (amongst others) as areas of applicability. From an analytics perspective, I define complex adaptive challenges as those that  are impacted by the execution of the analytics – by doing the analysis, the observed behaviors change. This is inherently true of fraud as the moment perpetrators  understand (or believe) they can be detected, behavior will change. However, it also applies to a host of other type of challenges: criminal activity, regulatory compliance enforcement, national security; as well as things like consumer marketing and financial investment.

In an article titled Images & Video: Really Big Data the authors (Fritz Venter the director of technology at AYATA; and Andrew Stein the chief adviser at the Pervasive Strategy Group. define an approach they call “prescriptive analytics” that is ideally suited to adaptive challenges. They define prescriptive analytics as follows:

“Prescriptive analytics leverages the emergence of big data and computational and scientific advances in the fields of statistics, mathematics, operations research, business rules and machine learning. Prescriptive analytics is essentially this chain of transformations whereby structured and unstructured big data is processed through intermediate representations to create a set of prescriptions (suggested future actions). These actions are essentially changes (over a future time frame) to variables that influence metrics of interest to an enterprise, government or another institution.”

My less wordy definition:  adaptive approaches deliver a broad set of analytical capabilities that enables a diverse set of integrated techniques to be applied recursively.

What Does the Robust Solution Look Like?

Defining adaptive analytics this way, one can identify characteristics of the ideal “robust” solution as follows:

  • A solution that builds out a framework that supports the broad array of techniques required.
  • A solution that is able to deal with the the challenges of recursive processing. This is very data and systems intensive. Essentially for every observation evaluated, the system must determine whether or not the observation changes any PRIOR observation or assertion.
  • A solution that engages users and subject matter experts to effectively integrate business rules. In an environment where traditional predictive analytic models have a short shelf life (See Note 1), engaging with the user community is often the mechanism to quickly capture environmental changes. For example, in the banking world, tracking call center activity will often identify changes in fraud behavior faster than a neural network set of models. Engaging the User in the analytical process will require user interfaces, and data visualization approaches that are targeted at the user population, and integrate with the organization’s work processes. Visualization will engage non technical users to help them apply their experience and intuition to the data to expose insights. The census bureau has an interesting page, and if you look at Google Images, you can get an idea of visualization approaches.
  • A solution that provides native support for statistical and mathematical functions supporting activities associated with data mining : clustering, correlation, pattern discovery, outlier detection, etc.
  • A solution that structures unstructured data: categorize, cluster, summarize, tag/extract. Of particular importance here is the ability to structure text or other unstructured data into taxonomies or ontologies related to the domain in question.
  • A solution that persists data with the rich set of metadata required to support complex analytics. While it is clearer why unstructured data must be organized into a taxonomy / ontology, this also applies to structured data. Organizing data consistently across the variety of sources allows non obvious relationships to be exposed, and application of more complex analytical approaches.
  • A solution that is relatively data agnostic  – data will come from many places and exist in many forms. The solution must manage the diversity and provide a flexible way to integrate new data into the analytical framework.

What are Candidate Tools ?

And now to the second question: where are these tools? It is hard to find tools that claim to be “adaptive analytic” tools; or “prescriptive analytics” tools or systems in the sense that I have described them above. I find it interesting that over the last five years, major vendors have subsumed complex analytical capabilities into a more easily understandable components. Specifically, you used to be able to find Microsoft  Analytical Services easily on their site. Now it is part of MS SQL Server as SSAS; much the same way that the reporting service is now part of the database offer as SSRS (reporting services). There was a time a few years ago when you had to look really hard on the MS site to find Analytical Services. Of course since then Microsoft has integrated various BI acquisitions into the offer and squared away their marketing communication. Now their positioning is squarely around  BI and the database. Both of these concepts are easier to sell at the executive level, than the notion of prescriptive or adaptive analytics.

The emergence of databases and appliances optimized around analytics has simplified the message on the data side. everyone knows they need a database, and now they have one for analytics. At the decision maker level, that is a much easier decision than trying to figure out what kind of analytical approach the organization is going to adopt. People like Teradata have always supported analytics through the integration of SAS and now R as in-database functionality. However, Greenplum, Neteeza and others have incorporated SAS and the open source analytical “R” . In addition, we have seen the emergence (not new but much more talked about it seems) of the columnar database. The one I hear about most is the Sybase IQ product; although there have been a number of posts on the topic on here, here, and here.

My point here is that vendors have too hard a time selling complex analytical solutions, and have subsumed the complex capabilities into the concepts that are easier to package, position and communicate around; namely; database products and Business Intelligence products. The following are product sets that are candidates for the integrated approach. We start with the big players first and work towards that are less obviously candidates.

SAS

The SAS Fraud Framework provides an integration of all the SAS components that required to implement a comprehensive analytics solution around adaptive challenges (all kinds of fraud, compliance, money laundering, etc. as examples). This is a comprehensive suite of capabilities that spans all activities: data capture, ingest, and quality; analytics tools (including algorithm libraries), data visualization and reporting / BI capabilities. Keep in mind that SAS is a company that sells the building blocks, and the Fraud Framework is just that, a framework within which customers can build out capabilities. This is not a simple plug and play implementation process. It takes time and investment and the right team within the organization. The training has improved, and it is now possible to get comprehensive training.

As with any implementation of SAS, this one comes with all the caveats associated with comprehensive enterprise systems that integrate  analytics into the fabric of an organization. The Gartner 2013 BI report indicates that SAS “very difficult to implement”. This theme echoes across the product set.  Having said that   when it comes to integrated analytic of the kind we have been discussing all, of the major vendors suffer from the same implementation challenges – although perhaps for different reasons.

Bottom line however, is that SAS is a company grounded in analytics – the Fraud Framework has everything needed to build out a first class system. However, the corporate culture builds products for hard core quants, and this is reflected in the Gartner comments.

IBM

IBM is another company that has the complete offer. They have invested heavily in the analytics space, and between their ETL tools; the database/ appliance and Big Data capabilities; the statistical product set that builds off SPSS; and, the Cognos BI suite users can build out the capabilities required. Although these products are being integrated into a seamless set of capabilities, they remain somewhat separate and this probably explains some of the implementation challenges reports. Also, the product side of the IBM operation does not necessarily speak with the Global Services side of the house.

I had thought when IBM purchased Systems Research & Development (SRD) in 2005 that they were going to build out capabilities that SRD and Jeff Jonas had developed. Jeff heads up the Entity Analytics group within IBM Research, and his blog is well worth the read. However, the above product set appears to have remained separated from the approaches and intellectual knowledge that came with SRD. This may be on purpose – from a marketing perspective, buy the product set, and then buy IBM services to operationalize the system is not a bad approach.

Regardless, as the saying goes, no one ever got fired for buying IBM” probably still holds true. However, like SAS beware of the implementation! Any one of the above products (SPSS, Cognos, and Infosphere) require attention when implementing. However, when integrating as an operational whole, project leadership needs to ensure that expectations as to the complexity and time frame are communicated.

Other Products

There are many other product sets and I look forward to learning more about them. Once I post this, someone is going to come back and mention “R” and other open source products. There are plenty out there. However, be aware that while the products may be robust, many are not delivered as an integrated package.

With respect to open source tools, it is worth noting that the capabilities inherent in Hadoop – and the related products, lend themselves to adaptive analytics in the sense that operators can consistently re-link and re-index on the fly without having to deal with where and how the data is persisted. This is key in areas like signals intelligence, unstructured data analysis, and even structured data analysis where the notion of semantic equivalence is shifting. This is a juicy topic all by itself and worthy of a whole blog entry.

Notes:

  1. Predictive analytics relies on past observations to predict future observations. In an adaptive environment, the inputs to those predictive models continually change as a result of the outputs using the past observations.

Evaluating Different Persistence Methods as part of the Planning Process

4 Jul

Every once in a while, I get asked about how to select between different types of databases. Generally, this comment is as a result of a product vendor or consultant making a recommendation to evolve towards a Big Data solution. The issue is twofold in that companies seek to understand what the next generation data platform looks like; AND, how or if their current environment can evolve. This involves understanding the pros and cons of the current product set and to what degree they can exist with newer approaches – Hadoop being the current platform people talk about.

The following is a list of data persistence approaches that helps at least define the options. This was done some time ago, so I am sure the vendors shown have evolved. However, think of it as a starting point to frame the discussion.

In general, one wants to anchor these discussions in some defined criteria that can help frame the discussion within the context of  business drivers. In the following figure, the goal is to show that as data sources and consumers of your data expand to include increasingly complex data structures and “contexts,” there is a need to evolve approaches beyond the traditional relational database (RDBMS) approaches. Different organizations will have different criteria. I provide this as a rubric that has worked before – you will need to create an approach that works for your organization or client.

Evolution of Data Persistence

A number of data persistence approaches  support the functional components as defined. These are described below.

Defined Pros / Cons Vendor examples
Relational Databases

(Row Orientation)

Traditional normalized data models optimized for efficiently storing data Relational structures are best used when the data structures are known and change infrequently. Relational designs often present challenges for analysts when queries and joins are executed that are incompatible with the design schema and / or indexing approach. This incompatibility creates processing bottlenecks, and resource challenges resulting in delays for data management teams. This approach is challenged when dealing with complex semantic data where multiple levels of parent / child relationships exist.

Advantages: This approach is best for transactional data where the relationships between the data and the use cases driving how data is accessed and used are stable. In uses where relational integrity is important and must be enforced in a consistent manner, this approach can work well. In a row based approach, contention on record locking are easier to manage than other methods.

Disadvantages: As the relationships between data and relational integrity are enforced through the application of a rigid data model, this approach is inflexible, and changes can be hard to  implement.

All major database vendors: IBM – DB2; Oracle; MS SQL and others
Columnar Databases

(Column Oriented)

Data organized  or indexed around columns; can be implemented in SQL or a NoSQL environments. Advantages: Columnar data designs lend themselves to analytical tasking involving large data sets where rapid search, retrieval and aggregation type queries are performed on large data tables. A columnar approach inherently creates vertical partitioning across the datasets stored this way. It is efficient and scalable.

Disadvantages: efficiencies can be offset by  the need to join many queries to obtain the desired result.

•Sybase IQ

•InfoBright

•Vertica (HP)

•Par Accel

•MS SQL 2012

Defined Pros / Cons Vendor examples
RDF Triple Stores / Databases Data stored organized around RDF triples (Actor-action-object OR Subject-predicate-Object); can be implemented in SQL or a NoSQL environments. Advantages: A semantic organization of data lends itself to analytical and knowledge management tasks where the understanding of complex and evolving relationships is key. This is especially the case where ontologies or SKOS (1) type relationships are required to organize entities and their relationships to one another: corporate hierarchies/networks; insider trading analysis for example. This approach to organizing data is often represented in the context of the “semantic web” whose organizing constructs are RDF and OWL. when dealing with complex semantic data where multiple levels of parent / child relationships exist, this approach is more efficient that RDBMS

Disadvantages: This approach to storing data is often not as efficient as relational approaches. It can be complicated to write queries to traverse complex networks – however, this is often not much easier in relational databases either.

Note: these can be implemented with XML  formatting or in some other form.

Native XML  / RDF Databases

•Marklogic (COTS)

•OpenLink Virtuoso (COTS)

•Stardog (o/s, COTS)

•BaseX  (o/s)

•eXist  (o/s)

•Sedna (o/s)

XML Enabled Databases

•IBM DB2

•MS SQL

•Oracle

•PostgrSQL

XML enabled databases deal with XML as a CLOB in a table or organized into tables based on a schema

Graph Databases A database that uses graph structures to store data. See XML / RDF Stores / Databases. Graph Databases are a variant on this theme.

Advantages:  Used primarily to store information on networks. Optimized for iterative joins; often in a recursive process (2)..

Disadvantages: Storage challenges – these are large datasets; builds through iterative joins – very processor intensive.

•ArangoDB

•OrientDB

•Cayley

•Aurelius Titan

•Aurelius Faunus

•Stardog

•Neo4J

•AllegroGraph

(1) SKOS = Simple Knowledge Organization Structure. Relationships can be expressed as triples; examples are “is part of”; “is similar to”

(2) Recursion versus iteration

Defined Pros / Cons Vendor Examples
NoSQL

File based storage – HDFS

Data structured to expose  insights through the use of “key pairs” This has many of the characteristics of the XML, Columnar and Graph approaches. In this instance, the data is loaded, and key value pair (KVP) files created external to the data. Think of the KVP as an index with a pointer back to the source data. This approach is generally associated with the Hadoop / MapReduce  capabilities, and the definition here assumes that KVP files are queried using the capabilities available in the Hadoop ecosystem

Advantages: flexibility; MPP capabilities; speed; schema-less; scalable; Great at creating views of data; and performing simple calculations across Big Data; significant open source community – especially through the Apache Foundation. Shared nothing architecture optimizes the read process. However, it creates challenges in meeting ACID (1) requirements. File based storage systems adhere to the BASE (2) requirements

Disadvantages: Share nothing architecture creates complexity in uses where sequencing of transactions or writing data is important – especially when multiple nodes are involved; complex metadata requirement; few tool “packages” available to support production environments; relatively immature product set.

Document Store

•Mongo DB

•Couch DB

Column Store

•Cassandra

•Hbase

•Accumulo

Key Value Pair

•Redis

•Riak

(1) ACID = Atomicity; Consistent; Isolated; Durable. Used for Transaction processing systems.

(2) BASE = Basic Availability, Soft State; Eventual Consistency. Used for distributed parallel processing systems where maintaining complete consistency is often prohibitively expensive

Defined Pros / Cons Vendor examples
In-Memory Approaches Data approaches where the data is loaded into active memory to improve efficiency Note that multiple persistence approaches can be implemented in memory

Advantages: Speed; flexibility – ability to virtualize views and calculated / derived tables; think of Datamarts in the traditional BI context

Disadvantages: Hardware, cost

•SAP HANA

•SAS High Performance Analytics

•VoltDB

The classes of tools below are presented as they provide alternatives for capabilities that are likely to be required. Many of the capabilities are resident in some of the tool sets already discussed.
Data Virtualization The ability to produce tables or views without going through an ETL process Data  virtualization is a capability built into other products. Any In- Memory product inherently virtualizes data. Likewise a number of the Enterprise BI tools allow data – generally in the form of “cubes” to be virtualized. Denodo Technologies is the major pure play vendor. The others vendors generally provide products that are part of larger suites of tools. •Composite Software (Cisco)

•Denodo Technologies

•Informatica

•IBM

•MS

•SAP

•Oracle

Search Engines Data management components that are used to search structured and unstructured data Search engines and appliances perform functions as simple as indexing data, and as complex as Natural Language Processing (NLP) and entity extraction. They are referenced here as the functionality can be implemented as stand alone capability and may be considered as part of the overall capability stack. •Google Search Appliance

•Elastic Search

Defined Pros / Cons Vendor examples
Hybrid Approaches Data products that implement both SQL and NoSQL approaches These are traditional SQL database approaches that have been partnered with one or more of the approaches defined above. Teradata acquired Aster to create a “bolt on” to a traditional SQL Db; IBM has Db2/Netezza/Big Insights. SAS uses a file based storage system and has created “Access Modules” that work though Apache HIVE to apply analytics within either an HDFS environment, or the SAS environment.

Another hybrid approach is exemplified by Cassandra that incorporates elements of a data model within a HDFS based system.

One also sees organizations implementing HDFS / RDBMS solutions for different functions. For example acquiring, landing and staging data using an HDFS approach, and then once requirements and the business use is known creating structured data models to facilitate and control delivery

Advantages: Integrated solutions; ability to leverage legacy; more developed toolkits to support production operations. Compared to open source, production ready solutions require less configuration and code development.

Disadvantages: Tend to be costly; architecture tends to be inflexible – all or nothing mindset.

•Teradata

•EMC

•SAS

•IBM

•Cassandra (Apache)

Old School vs. new school – Its Both!

24 Oct

Excellent article by Wayne Eckerson (most of his are) . We give Data Warehouses a bad name because they have been implemented in a way that does not meet the businesses needs – certainly not from an analytical perspective. HOWEVER, the business reasons that they exist remain, and this is Wayne’s point. I have been watching the shouting match between Inmon and Kimball.  I think they are both wrong – the answer is not as simple as they make it out to be – our world will be hybrid SQL/RDBMS and NoSQL and everything will need to play nice together! Those are my words  of wisdom on a Friday 🙂

A comparison of programming languages in economics

8 Jul

Interesting comparison of programming language speeds. Given that the big data world seems to be all about Python, I wonder if folks start doing complicated calculations over big data if they will move away from Python? SAS is apparently working on “Accelerators” to work on hadoop nodes which appear to address this same problem. They already have them for Databases and Db appliances.

The above makes sense if you consider that for the most part “big Data” is about folks doing simple calculations in parallel  over many data nodes.

The thread of comments below the article are also interesting.

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There is a new NBER working paper with that title, by S. Borağan Aruoba and Jesus Fernandez-Villaverde. Here is the abstract:

We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++11, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration with grid search, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and comment on the strength and weakness of each language.

Here are their results:

1. C++ and Fortran are still considerably faster than any other alternative, although one needs to be careful with the choice of compiler.

2. C++ compilers have advanced enough that, contrary to the situation in the 1990s and some folk wisdom, C++ code runs slightly faster (5-7 percent) than Fortran code.

3. Julia, with its just-in-time compiler, delivers outstanding per formance. Execution speed is only between 2.64 and 2.70 times the execution speed of the best C++ compiler.

4. Baseline Python was slow. Using the Pypy implementation, it runs around 44 times slower than in C++. Using the default CPython interpreter, the code runs between 155 and 269 times slower than in C++.

5. However, a relatively small rewriting of the code and the use of Numba (a just-in-time compiler for Python that uses decorators) dramatically improves Python ’s performance: the decorated code runs only between 1.57 and 1.62 times slower than the best C++ executable.

6.Matlab is between 9 to 11 times slower than the best C++ executable. When combined with Mex files, though, the difference is only 1.24 to 1.64 times.

7. R runs between 500 to 700 times slower than C++ . If the code is compiled, the code is between 240 to 340 times slower.

8. Mathematica can deliver excellent speed, about four times slower than C++, but only after a considerable rewriting of the code to take advantage of the peculiarities of the language. The baseline version our algorithm in Mathematica is much slower, even after taking advantage of Mathematica compilation.

There are ungated copies and some discussion here.

 

 

The different aspects of BI

5 Dec

http://www.martinsights.com/?p=774

I like the recognition that approaches need to be integrated in order to create useful insights. Valuable insights come from balancing the needs and capabilities of  business strategy, business analysis, business intelligence and advanced analytics.

The addition of analytical functions to databases

14 Nov

The trend has been for database vendors to integrate analytical functions into their products; thereby moving the analytics closer to the data (versus moving the data to the analytics). Interesting comments in the article below on Curt Monash’s excellent blog.

What was interesting to me, was not the central premise of the story that Curt does not  “think [Teradata’s] library of pre-built analytic packages has been a big success”, but rather the BI vendors that are reportedly planning to integrate to those libraries: Tableau, TIBCO Spotfire, and Alteryx. This is interesting as these are the rapid risers in the space, who have risen to prominence on the basis of data visualization and ease of use – not on the basis of their statistical analytics or big data prowess.

Tableau and Spotfire specifically focused on ease of use and visualization as an extension of Excel spreadsheets. They have more recently started to market themselves as being able to deal with “big data” (i.e. being Hadoop buzzword compliant). With the integration to a Teradata stack and presumably integrating front end functionality into some of these back end capabilities, one might expect to see some interesting features. TIBCO actually acquired an analytics company. Are they finally going to integrate the lot on top of a database? I have said it before, and I will say it again, TIBCO has the ESB (Enterprise Service Bus), the visualization tool in Spotfire and the analytical product (Insightful); hooking these all together on a Teradata stack would make a lot of sense – especially since Teradata and TIBCO are both well established in the financial sector. To be fair to TIBCO, they seem to be moving in this direction, but it has been some time since I used the product).

Alteryx is interesting to me in that they have gone after SAS in a big way. I read their white paper and downloaded the free product. They keep harping on the fact that they are simpler to use than SAS, and the white paper is fierce in its criticism of SAS. I gave their tool a quick run through, and came away with two thoughts: 1) the interface while it does not require coding/script as SAS does, cannot really be called simple; and 2) they are not trying to do the same things as SAS. SAS occupies a different space in the BI world than these tools have traditionally occupied. However,…

Do these tools begin to move into the SAS space by integrating onto foundational data capabilities? The reason SAS is less easy to use than the products of these rapidly growing players is that the rapidly growing players have not tackled the really tough analytics problems in the big data space. The moment they start to tackle big data mining problems requiring complex and recursive analytics, will they start to look more like SAS? If you think I am picking on SAS, swap out SAS for the IBM Cognos, SPSS, Netezza, Streams, Big Insights stack, and see how easy that is! Not to mention the price tag that comes with it.

What is certain is that these “new” players in the Statistical and BI spaces will do whatever they can to make advanced capabilities available to a broader audience than traditionally has been the case with SAS or SPSS (IBM). This will have the effect of making analytically enhanced insights more broadly available within organizations – that has to be a good thing.

Article Link and copy below

October 10, 2013

Libraries in Teradata Aster

I recently wrote (emphasis added):

My clients at Teradata Aster probably see things differently, but I don’t think their library of pre-built analytic packages has been a big success. The same goes for other analytic platform vendors who have done similar (generally lesser) things. I believe that this is because such limited libraries don’t do enough of what users want.

The bolded part has been, shall we say, confirmed. As Randy Lea tells it, Teradata Aster sales qualification includes the determination that at least one SQL-MR operator — be relevant to the use case. (“Operator” seems to be the word now, rather than “function”.) Randy agreed that some users prefer hand-coding, but believes a large majority would like to push work to data analysts/business analysts who might have strong SQL skills, but be less adept at general mathematical programming.

This phrasing will all be less accurate after the release of Aster 6, which extends Aster’s capabilities beyond the trinity of SQL, the SQL-MR library, and Aster-supported hand-coding.

Randy also said:

  • A typical Teradata Aster production customer uses 8-12 of the prebuilt functions (but now they seem to be called operators).
  • nPath is used in almost every Aster account. (And by now nPath has morphed into a family of about 5 different things.)
  • The Aster collaborative filtering operator is used in almost every account.
  • Ditto a/the text operator.
  • Several business intelligence vendors are partnering for direct access to selected Teradata Aster operators — mentioned were Tableau, TIBCO Spotfire, and Alteryx.
  • I don’t know whether this is on the strength of a specific operator or not, but Aster is used to help with predictive parts failure applications in multiple industries.

And Randy seemed to agree when I put words in his mouth to the effect that the prebuilt operators save users months of development time.

Meanwhile, Teradata Aster has started a whole new library for relationship analytics.

Healthcare’s New Big Idea

14 Oct

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Once upon a time in the American healthcare system, big data was an unknown idea. Recognizing that healthcare costs rose unmanageably and healthcare quality varied dramatically without clear explanation, Congress introduced Managed Care with the hope that relying upon a for-profit business model would make the system more competitive, more comprehensive, and more effective. Now, over thirty years later, it appears that new changes in American healthcare will position “big data” as the driver of effectiveness and competitiveness. Here are a few reasons why.

When thinking about the government policy that will make big data essential in the new healthcare system, three main pieces of legislation come to mind – the obvious heavyweight of the group being the Affordable Care Act (ACA). By now, most know that by passing the ACA into law, the federal government shifts America away from a volume-based system of care (in which doctors and hospitals make money based on how many tests they run and treatments they try) to a value-based system in which doctors and hospitals receive rewards according to the value created for patients. However few know that in order to actualize this value-based system, the ACA directly implicates big data at federal and state levels of healthcare. For example, the ACA authorizes the Department of Health and Human Services (HHS) to release decades of stored data and make it usable, searchable, and ultimately analyzable by the health sector as a whole to promote transparency in markets for healthcare and health insurance. Here, the driver of transparency, and thus competitiveness and effectiveness, is clearly big data.

In other examples, the ACA uses language that endorses, if not mandates, big data use throughout the system. The ACA not only explicitly authorizes the Center for Disease Control (CDC) to “provide grants to states and large local health departments” to conduct “evidence-based” interventions, it creates a technical assistance programs to diffuse “evidence-based therapies” throughout the healthcare environment. Note that in the medical community, “evidence-based medicine” means making treatment decisions for individual patients based on data of the best scientific evidence available, rendering the use of this relatively new term an endorsement of big data in healthcare treatment. These pieces of evidence – in the form of direct references to big data at the federal level, state level, and patient level – strongly support the conclusion that the ACA creates a new system reliant upon big data for efficiency and competitiveness.

The remaining pieces of legislation further signal big data as the new lifeblood of the American healthcare environment. In 2009, the Open Government Directive, in combination with the Health Data Initiative implemented by HHS, called for agencies like the Food and Drug Administration (FDA), Center for Medicare & Medicaid Services (CMS), and CDC to liberate decades of data. The Health Information Technology for Economic and Clinical Health Act (HITECH), part of the 2009 American Recovery and Reinvestment Act, authorized over $39 billion in incentive payments for providers to use electronic medical records, with the goal of driving adoption up to 85% by 2019. Finally, to facilitate the exchange of information and accelerate the adoption of data reliance in the new health environment, CMS created the Office of Information Products and Data Analytics to oversee numerous databases and collaborate with the private sector. Among other functions, this office will oversee the over $550 million spent by HHS to create data clearinghouses – run by states – that will consolidate data from providers within the given state. All of this legislation, which essentially produces a giant slot for a big data peg to fill, paves the way for a new healthcare environment reliant upon rapid sharing, analysis, and synthesis of large quantities of community and national health data.

Now at this point, nearly four years after legislation supposedly opened the floodgates of big healthcare data to the private sector, the reader must wonder why more private sector companies haven’t taken advantage of an obvious market opportunity. The answer is: actually, a few first movers have.

Blue Cross / Blue Shield of California, working together with a company called Nant Health, has created an integrated technology system that allows hospitals, HMOs, and doctors to deliver evidence-based care to patients under their jurisdiction. This system catalyzes performance improvement, and thus revenue-generating value creation, across the system. The use of big data has also allowed some first movers to innovate and generate applications reliant upon newly liberated data. A company called Asthmapolis created a GPS-enabled tracker that monitors inhaler usage by asthmatics, directs the data into a central database used to detect macro-level trends, and merges the data with CDC data pertaining to known asthma triggers. These few cases illustrate that private sector engagement in this new market opportunity remains new, and diverse, and far from delimited.

The ACA has moved into its execution phase, and the introduction of the big data idea poses new and interesting challenges to how the American Healthcare system will evolve. Some challenges will bring about positive change, such as identification or clear opportunities for preventive care. Other challenges will bring negative change, such as the adverse effects transparency will likely have on certain patient groups. Regardless, it looks like big data is here to stay.

Primer on Big Data, Hadoop and “In-memory” Data Clouds

25 Aug

This is a good article. There have been a number of articles recently on the hype of big data, but the fact of the matter is that the technology related to what people are calling “big data” is here to stay, and it is going to change the way complex problems are handled. This article provides an overview. For those looking for products, this has a good set of links.

This is a good companion piece to the articles by Wayne Eckerson referenced in this post

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