Posted in Big Data

Big Data Post #9 – Case study : 1010Data ☆。゚+.(人-ω◕ฺ)゚+.゚




( ´థ౪థ)σ’`゙1010Dɐʇɐ


1010data provides a cloud-based platform for big data discovery and data sharing that delivers actionable, data-driven insights quickly and easily.

**It is a website (‘-.-)**

What is 1010data?

This might seem like a strange question to ask, but the fact is that 1010data is not an easy technology to classify. At the moment there are no products or services that are similar in overall capability to 1010data, although there are quite a few products which overlap some areas of 1010data’s application. The broad spread of 1010data’s capability has, in our view, led some industry analysts to misunderstand the technology.

  • High Performance “Big Data” DBMS – Netezza

1010data fulfills all three roles, whereas the products with which it might be compared can at best claim to fulfill two of these roles. So you can use 1010data as a database that simply satisfies the query demands of a whole clutch of BI tools and you can also employ it as an analytical database that carries out analytical calculations, and many 1010data customers do.

However, in almost every case, 1010data’s customers buy the 1010data service for its interactive analytics capabilities. 1010data delivers a high performance Interactive Analytical Database and in that respect, it is unique. There are analytical products like Qlikview or Tableau or even MS Excel, that provide an interactive analytical capability, but they do not sit over very large volumes of data. They tend to sit over subsets of data drawn from a data warehouse or data mart.

1010data is interactive in exactly the same way that such PC-based products are. The user does something, such as pose a question, and the software responds quickly with the result. The user then does something with that result, such as join it to some other data, and again the software responds quickly. This way of working is highly productive, allowing a user to explore the data in ways that would be impossible without an interactive interface and fast database responses.


How did it evolve?

Given the uniqueness of 1010data, it is interesting that the evolution of the technology has been primarily customer-driven. The software engineers, Joel Kaplan and Sandy Steier, who built 1010data, entered the software industry from the IT user’s side. Originally they were employed by the Wall Street bank, Morgan Stanley, as part of the bank’s Analytical Systems Group, and there they pioneered the use of software as an enabling capability for financial traders.

That was back in the 1980s, when neither the PC nor the spreadsheet were widely used, Windows didn’t exist, and Unix servers were as rare as ATMs in Antarctica. 

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Nevertheless, in those days it was possible, using the IBM mainframe, to apply analytics to data and present the results on a green screen. The analytical capability that Wall Street traders wanted at the time was spreadsheet-like in a way. They wanted to manipulate large tables of financial data, sorting it, filtering it and applying analytical functions to it.

Moving from one employer to another, Kaplan and Steier built software that provided such capabilities for many years before they founded 1010data to build their own technology based on their experience and expertise in those financial environments. And naturally, from the get go, they sold 1010data to companies in the financial sector.

The technology didn’t start life in some other location on that diagram and gradually migrate to the center. It was designed from the start to be an interactive analytical database.

Another Rare Capability

In addition to the 3 previously mentioned characteristics in order that 1010Data can be studied thoroughly, Database As A Service can also be include into which, as far as I am aware, only 1010data would fit.
1010data could be characterized as “cloud technology” in the sense that the software is not often deployed in the customer’s data center. The company has its own cloud data center and in most customer engagements, it loads the customer’s data and runs the software from its data center, while users within the customer organization log in and use the system via a browser.
In practice, 1010data has a minimal to almost zero requirement for Database Administrators. The company forges an agreement on where data will be sourced from and what the update cycle will be, then 1010data’s consultants configure and load the data into the database and make the software available to users within about a week, or sooner. So 1010data is primarily a cloud-based service and has been since the company was founded in 2000.
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Nevertheless, 1010data is also deployed and used in other ways. It can be deployed as an appliance, in which case 1010data will deliver a server or cluster of servers to the customer so that the hardware is co-located in the customer’s data center, but 1010data will still manage the running of the database.
Within 1010data’s cloud data center, a customer can either trust 1010data to locate the database or, at extra cost, have its own hardware so that none of its resources are shared. Another possibility is that the customer simply purchase data and analytical capability as a service. There are a number of generally useful databases that 1010data hosts, particularly databases of financial data, which many customers use. In those circumstances, 1010data is making that data available as a service on behalf of its owner and the customers are licensees that have bought the right to use the data and 1010data’s analytical capabilities. Many such licensees also run 1010data on their own data, because they wish to analyze their own data in conjunction with the data that they have licensed access to.
Finally there are some customers on behalf of whom 1010data runs as a private licensing arrangement, where the customer, a retailer for example, makes their sales data available as a service to some of their suppliers. This is similar to the licensing arrangement described above, but the customer controls who is able to use the data and can thus vary the licensing terms to suit its own business priorities. 

Areas of Application for 1010data

1010data’s natural areas of application are anywhere where the speedy analysis of large collections of data will deliver value. Industry sectors where such activity can make a significant contribution naturally include: telecommunications, banking, financial trading, insurance, utilities, retail and government. It also includes all areas of Internet activity from ecommerce to gaming and social networks, where large amounts of data are gathered on a daily basis and can be usefully analyzed.
Taking a different tack, we can think of 1010data’s area of application in terms of general business activities, where analytics is already part of the process or where in the future it is likely to bubble up, such as:

  • Risk Management: Risk management has, in recent years, grown from being an activity that was associated with banking, where risk was always the nature of the business, to almost any area of commerce from pharmaceuticals to energy that involves large investments and/or government regulation. Investment by its very nature, is a risky activity; so where there is large investment there is commercial risk. Risk management virtually demands the analysis of large amounts of data in order to quantify and minimize risk.
  • Sales and Marketing: This has always been an application area for analytics. In recent times, computers have become powerful enough to analyze sales at a customer level on an item-by-item basis. Now, this is even the case in the telecommunications industry where the basic customer purchase is a single telephone call or text message. Because of its big data capability, 1010data is obviously suited to this area of activity.
  • Security: This is another area where the volume of data has, until recently, defeated attempts to do analysis in a timely manner. In computer networks security breaches nearly always involve anomalous behavior. And that’s also true of credit card fraud. Indeed, it’s true of most instances of commercial fraud, money laundering and other nefarious activity.
  • Machine Generated Data: This is an area of extraordinary data growth in recent times, whether you look at computer gaming, network activity in a computer network, RFID data or just usage logs on web sites. 1010data is relevant here by virtue of speed of analysis and the fact that it can handle very large amounts of data.
  • Data as a Service. There are already quite a few companies in many sectors of the economy whose business is in gathering data and making it available as a service. 1010data has the advantage that it assists both in the managing and distributing such data and in providing a wealth of analytics capability that can be extended to any customer.

I think that should be enough for today?

o(- -;*)ゞ

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