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Wednesday, May 23, 2012

The Rise of the Connector - Part 2

Connectors are the heart and soul of Federated Search (FS) engines and with the rise in importance of FS in today’s fast paced, Big Data, analyze everything world, they are crucial to smooth and efficient data virtualization and flow. MuseGlobal has been building Connectors, and the architecture to use them (the Muse/ICE platform) and maintain and support them (the Muse Source Factory) for over 12 years. The people who design and build Connectors must be both computer savvy, and also have a deep understanding of data and information and its myriad formulations.

This second in the series of posts looks at the problems arising as data is needed from outside the enterprise, and the complexities of access and extraction that result. Not surprisingly, as a leading FS platform Muse and its ecosystem are in the forefront of providing solutions to data complexity problems in the modern world. (The first post considers the growing importance of being able to access data from inside an organization.)

Part 2         A broader perspective

All of this speaks to the volume and velocity (of change) of the data – two of the trio of defining “v”s of Big Data. The third v is variety and this is now encompassing much more than the internal data silos of the enterprise. Increasingly decisions need to take account of the outside world: competitors, news media, commentators and analysts, customer feedback, social postings and tweets.

Most of these sources are also fleeting. Customer records will last for years, a tweet is gone in 9 days. Even product reviews are only relevant until the next version of the product is released. And there are another couple of additional hurdles to jump to get this valuable “perspective” data.

This data lives outside the enterprise. Some other person or organization has control of it. And that means the old ETL trick of grabbing everything is likely to be severely frowned on – especially if it is tried every night. Commercial considerations mean that, if this data is valuable to you, then it is valuable to others, and the owners will not let you have it all for free. This means the strategy of asking for exactly what is needed is the way to go. It takes less time everywhere, will cost less in processing and transmission, will cost less in data license fees, and will not alienate valuable data sources. So “sipping gently” is the way to go.

Yes, in the paragraph above you saw “fees” mentioned. Once the commercial details have been sorted out, there is still the tricky technical matter of getting access through the paywall to the data you need, and are entitled to. Some services will provide some of the data you want for free, but most will require authenticated access even of there is no charge Those who are selling their data will certainly want to know that you are a legitimate user, and be sure you are getting what you have paid for – and no more.

For both of these considerations Federated Search engines, especially in their harvesting mode allow all the “virtual data” to become yours when you need it. Access control is one of the mainstays of the better FS systems to ensure just this fair use of data. And gentle sipping for just the required data is their whole purpose. Again a tool for the task arises. MuseGlobal runs a Content Partner Program to ensure we deal fairly and accurately with the data we retrieve from the thousands of sources we can connect to, both technically and as a matter of respecting the contractual relationship between the provider and consumer. We are the Switzerland of data access – totally neutral and scrupulously fair, and secure.

Complexity everywhere

So now you are accessing internal and external data for your BI reports. Unfortunately, while you might have a nice clean Master Data Managed situation in your company, it is not the one the external data sources are using (not unless you are Walmart or GM and can impose your will on your suppliers, that is). And this means the analysis will be pretty bad unless you can get internal product codes to match to popular names in posts and tweets. There is a world of semantic hurt lurking here.

You need tools. Fortunately the Federated Search engine you are now employing to gather your virtual data is able to help. Data re-formatting, field level semantics, content level semantics, controlled ontologies, normalized forms, content merging and de-merging, enumeration, duplicate control, all these are tools within the FS system. They are powerful tools and they are very precise, and they come with a health warning: “This Connector is for use with this source only”.

Connectors are built, and maintained, very specifically for a single Target. They know all about that target, from its communications protocol to the abbreviations it uses in the data. Thus they produce the deepest possible data extraction possible. And can deliver that data in a consistent format suited to the Data Model and systems which are going to use it. They are data transformers extraordinaire. This contrasts with crawlers at the other end of the scale where the aim is to get a simple sufficiency of data to handle keyword indexing.

This precision means that they are in need of “tuning” whenever their target changes in some way. Major changes like access protocols are rare, but a website changing the layout of its reviews is common and frequent. Complexity like this is handled by a “tools infrastructure” for the FS engine whereby testing, modification, testing again, and deployment are highly automated actions, reducing the human input to the problem solving, not the rote.

And now another wrinkle: some of the data needed for the analysis is not contained in the records you retrieve, and the only way to determine this is to examine those records and then go and get it. As a simple example think of a tweet which references a blog post. The tweet has the link, but not the content of the post. For a meaningful analysis, you need that original post. Fortunately the better FS systems have a feature called enhancement which allows for just this possibility. It allows the system to build completely virtual records from the content of others. Think more deeply of a hospital patient record. This will have administrative details, but no financial data, no medical history notes, not results of blood tests, no scans, no operation reports, no list of past and current drugs. And even if you gather all this, the list of drugs will not include their interactions, so there could be more digging to do. A properly configured and authenticated FS system will deliver this complete record.

Analysis these days is more than just a list of what people said about your product. It involves demographics and sentiment, and timeliness and location. All these can come from a good analysis engine – if it has the raw data to work from. Enhanced virtual records from a wide spectrum of sources will give a lot, but making the connections may not be that simple. We mentioned above “official” and popular product names and the need to reconcile them. Think for a moment of drug names. Fortunately a good FS system can do a lot of this thinking for you, and your analytics engine. Extraction of entities by mining the unstructured text of reviews and posts and news article and scientific literature allows them to be tagged so that the analysis recognizes the sameness of them. Good FS engines will allow this to a degree. Better ones will also allow that a specialist text miner can be incorporated in the workflow and give each record its special treatment – all invisibly to the BI system asking for the data.

Partnership at last

There is a lot of data out there, and a great deal of it is probably very useful to you and your company. Using the correct analysis engines and Federated Search “feeding” tools enables that data to be brought together in a flexible, efficient, and accurate manner to give the information needed for informed decisions.

Federated Search is still a very powerful and effective way to search for humans, but it has grown up to be one of the most effective tools for systems integration, the breaking down of corporate silos of data, and the incorporation of data from the whole Internet into a unified, useable data set to create real knowledge.

Muse is one of those tools which can supply the complete range from end user fed search portals, to embedded data virtualization, and we intend to keep up with the next turn of data events.

1 comment:

Rick Campbell said...

Nice! And I would also like to draw your attention to data room due diligence and to cloud technology in general.