Category Archives: Telecommunications
I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application - especially middleware – to a business leader or even a CIO. Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts. Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts. Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data. They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.
If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how. Depending on the business model you may even have to figure out who built it or owns it. All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized. And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.
Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets. Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function. The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement. The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything. Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….
Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.
I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple. None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for. It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system. After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old. Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.
So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child? Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age. This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit. Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.
If you are aware of business models and their difficulties to leverage data, write to me. If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts. Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.
Most application owners know that as data volumes accumulate, application performance can take a major hit if the underlying infrastructure is not aligned to keep up with demand. The problem is that constantly adding hardware to manage data growth can get costly – stealing budgets away from needed innovation and modernization initiatives.
Join Julie Lockner as she reviews the Cox Communications case study on how they were able to solve an application performance problem caused by too much data with the hardware they already had by using Informatica Data Archive with Smart Partitioning. Source: TechValidate. TVID: 3A9-97F-577
One major emergence from the Big Data debate especially in the Telco Industry is the sudden elevation of the focus on Customer Experience with QoE or Quality of Experience and CEM or Customer Experience management. With new and emerging technologies such as Near Field Communication (NFC), Machine to Machine (M2M) and Mobile Social Media Apps hitting the news every day like a Reality ‘Stars’ socializing antics; we are all fascinated by how much organisations either know, can find out or deduce about our lives: what we like / dislike, how much we may be worth to those organisations, what we already own and even where we physically are or will be in the next few minutes. All the minutiae of our lives and personalities laid bare to be pawed over, analysed and used to control us and eventually sell us yet more ‘stuff’. (more…)
Remote Data Collection and Transformation – with Ultra Messaging Cache Option and B2B Data Transformation
Sometimes when I drive past an electronic tollway collection sensor, I wonder about the amount of data it must generate. I’m no expert on such technology, but at a minimum, the RFID sensor has to read the chip in your car, and log the date and time plus your RFID info, and then a camera takes a picture to catch any potential violators. Now multiply that data times the hundreds of thousands of cars that drive such roads every day, times the number of sensors they pass, and I’m quite sure this number exceeds several million messages per day. (more…)
Gartner hosted a webinar on January 10, 2012: Gartner Worldwide IT Spending Forecast. One of the topics covered was industry IT spend for 2012.
In covering that topic they made a point of saying that due to severe flooding in Thailand, they expect storage to become in short supply (as much as a 29% global shortfall) through the end of 2012. It is expected that the price of storage/GB will increase as a result and supplies will fall short of demand. They recommended finding alternatives to purchasing storage to keep costs down. (more…)
If you haven’t already, I think you should read The Forrester Wave™: Data Virtualization, Q1 2012. For several reasons – one, to truly understand the space, and two, to understand the critical capabilities required to be a solution that solves real data integration problems.
At the very outset, let’s clearly define Data Virtualization. Simply put, Data Virtualization is foundational to Data Integration. It enables fast and direct access to the critical data and reports that the business needs and trusts. It is not to be confused with simple, traditional Data Federation. Instead, think of it as a superset which must complement existing data architectures to support BI agility, MDM and SOA. (more…)
Similar to the way that a carburetor restrictor plate prevents NASCAR race cars from going as fast as possible by restricting maximum airflow, inefficient messaging middleware prevents IT organizations from processing vital business data as fast as possible.
Most Industry analysts agree that there will be a dramatic increase in the number and nature of ‘connected devices’ over the next 3-5 years. These are predicted to run into tens of billions and include communication-enabled consumer devices, machines that move around (vehicles), things that don’t (vending machines, street furniture) and a myriad of sensors, cameras and other intelligent devices.
And you know where there are things that are measuring stuff there will be data; enormous amounts of data; and people and systems wanting to integrate this data and analyse it. (more…)
In a recent InformationWeek blog, “Big Data A Big Backup Challenge”, George Crump aptly pointed out the problems of backing up big data and outlined some best practices that should be applied to address them, including:
- Identifying which data can be re-derived and therefore doesn’t need to be backed up
- Eliminating redundancy, file de-duplication, and applying data compression
- Using storage tiering and the combination of online disk and tapes to reduce storage cost and optimize performance (more…)
In the past, the term latency has been largely ignored in the IT world, with the exception of network engineers and algorithmic trading experts. But today, there is compelling evidence that latency is an important metric for every business that runs a website, or that deploys Rich Internet Applications (RIAs), because even small delays in presenting data show a clear pattern of pushing customers and readers away.
Interesting data, replicated by multiple sources (including Bing, Google, and Amazon) show that slow-loading pages can cause the viewer to lose focus and potentially even click on something else, possibly never to return.
For instance, on search results, a delay of just .5 second chases away up to 20% of the traffic and revenue. As it says at this O’Reilly Radar post, “delays under half a second impact business metrics”.