Business intelligence

  • Kerry Butters

Data is Meaningless Without Analysis
Data is Meaningless Without Analysis 150 150 Kerry Butters

There’s value in organisations being able to analyse social media information and compile profiles to better target their customers. But creating, documenting, and retrieving vast amounts of data is one thing. Understanding it is an entirely different matter.

Context is Key

Measuring ‘likes’ or searching for keywords and phrases is pretty straightforward – a “sentiment analysis”. You might be tempted to develop a marketing strategy directly derived from this.

But there’ll always be examples of impulse buys, or snap decisions in the heat of the moment. And data samples may include information that’s not so easy to quantify – like pictures or videos.

In fact, the majority of actions will be based on the context surrounding them. Brand A might cost less, but B offers greater satisfaction. The sports car looks great, but what about the kids? And so on.

If a data analysis tool can’t provide further context around the solutions it offers, it’s at best, an expensive waste of time.

Informed Decision-Making

Investments in data analytics can be useless – even harmful – unless employees can incorporate that data into complex decisions. Meeting this challenge requires an understanding of human behaviour which is often lacking – and not only in IT departments.

Operational Intelligence

On a par with business intelligence is the need for operational intelligence. The ability to see and know everything that’s happening in your IT environment, at any moment. A tall order, considering the levels of scale and complexity, involved.

But armed with this knowledge, IT teams can better collaborate, fix problems, and provide support for product launches, application rollouts, migrations, upgrades, and other initiatives.

Operational intelligence may be obtained from four primary data sources:

1.  Machine data: such as log files, SNMP and WMI. Data from sensors (e.g. on wearable devices) also applies.

2.  Code-level instrumentation: which traditional application performance management (APM) is based on.

3.  Service checks: which provide insights on whether applications are up or down, and how well they’re performing.

4.  Wire data: the data-in-motion, describing all communications between systems.

Of these, wire data has the greatest potential for transforming intelligence.

Get Wired!

Wire data is the record of everything that’s happening in IT, in real time. It provides an in-depth view into the performance, availability, and security of your environment – including issues you might otherwise be unaware of.

Wire data is unstructured, and also high-velocity; generally at 10Gbps in data centres, and faster still in cloud environments. Powerful packet processing capabilities are required, just to keep up.

But, with the right tools in place, it can assist with:

1. Detecting Application and Infrastructure Performance Issues: Based on communications over the wire.

2. Big Data Analysis: You can extract specific pieces of wire data and feed it into analysis platforms such as MongoDB or Splunk.

3. Spotting Data Theft: You can easily identify when data is being stolen from your back-end databases – a particularly vulnerable place. Using wire data, you can spot when queries are being made by unknown or untrusted sources.

4. Parsing Data: Using Big Data analysis tools, you can mine that data for business intelligence purposes. 

5. Generating Meaningful Reports: Which will enable you to analyse what’s happening, with the data you collect.

Visualisation

Visualisation helps put data into context and bring business cases to life, through the creation of visual models that represent what’s happening to and with your data.

Most people are now moving toward the kinds of models having dashboards of information where you can zoom in or out. These help to understand what happened or did not happen based on actions you took – a hindsight analysis.

To look into the future, visualisation models need to be more dynamic. 

In the Real World…

Let’s take utilities (power, gas supply etc.) as an example. Most have archaic records and inaccurate information, with no idea where all of their underground assets are located. That makes it hard for them to deal with service interruptions that can occur when a power line is accidentally cut, or a water main bursts.

In the USA, the Las Vegas city government has taken advantage of smart data to develop a living model of its utilities network. VTN Consulting helped the city aggregate data from various sources into a single real-time 3D model using Autodesk technology. The model is being used to visualise the location and performance of critical assets above and below ground.

Companies in the health sector are reporting impressive results as they use Big Data analytics to increase efficiency, improve patient outcomes and provide greater personal care. These are largely fuelled by the US government’s push for more meaningful use of electronic health record (EHR) systems.

How to Make Data Meaningful?

Work backwards, and ask a few fundamental questions:

·   What business processes or decisions do you want to improve? (Make sure to get management involved at this level.)

·   How will these decisions improve the business?

·   What are you trying to maximise?

·   What are the most meaningful elements used to measure progress toward those goals?

·   What types of analysis do you need to perform to expose the data, explore “what if” scenarios and work through alternatives to optimise your operations?

·   What types of data do you need to collect in order to feed the above analysis and decision-making?

  • Kerry Butters

Obituary: Big Data
Obituary: Big Data 150 150 Kerry Butters

Donald Feinberg, VP and analyst at Gartner’s Intelligence and Information Group, recently said that Big Data will die within the next couple of years, thanks largely to the confusion which surrounds the term.

Once upon a time, databases were relatively small; tiny by today’s standards. Businesses had records of their customers’ accounts, built up manually over time, originally with pen and paper and later with microprocessors. Bigger companies started to have whole floors dedicated to data processing departments, ensuring that purchase orders and invoices we all matched and accurate, and accountants knew who had paid and who owed money, what had been bought and what had been cancelled.

With cloud computing and processing technology getting so small that you could practically map out the life cycle of a grain of rice, data started to get recorded and collected at increasingly faster rates and much more of it. Processors in cars and other equipment meant that a whole boatload of parameters could be constantly measured.

More and more measurables

Social media sites, ecommerce sites and other communal online gatherings meant that individuals could be adding to the pile of data already stored about them as they filled in forms and registered for things online. Photos, likes, friends, birthdays, political leanings, sexual orientation, marriage status, hobbies and interests…the list of measurables became endless.

Marketers cottoned on that they could find out even more about people and their activities by giving a little entertainment in return for information.

Data was evolving and its new buzz-name was emerging. This thing was big and needed a grand, although quite unoriginal, title. ‘Big Data’ was born and every smart sales person, IT geek and technical consultant around was throwing the phrase around in conversations.

What to do with Big Data?

Some people had ‘Big Data’ but didn’t know what to do with it. Others had bigger ‘Big Data’ than everyone else (so there) and knew exactly what they wanted to do with it. Certain eager beavers didn’t care if it was called Data, Big Data or naught and one spaghetti; they just made sure that the IT infrastructure for their organisation could handle any amount of information that was going through their servers and conduits.

Others blissfully got on with running their businesses, hiring the services of IT support companies and other professionals who would make sure their systems didn’t crash and their printers worked when they turned them on. Some smart people were figuring out how to condense meaningful understanding from all the data they were gathering.

Changing technologies

This was Big Data’s heyday, and a time when it actually meant something. Technology was changing and much of the change related to the amount of data that was ‘out there’, how it was being managed, how quickly it could be processed and moved around, and the mind-blowing variety of variables that could be and where being measured. Surely this data was going to be extremely useful for managing situations, for making the most out of the trends, for future proofing organisations by learning lessons from the past.

In fact, the enormity of the situation meant it was almost beyond definition. From a business perspective many aspects of how information stock piles were growing could be a threat or an opportunity. It all depended on how businesses reacted to the changes and the trends, and embraced what was happening. There would be winners. There would be losers. Who would win and who would lose was down to how they played the Big Data lottery.

The death of Big Data?

Yes, they were halcyon days for ‘Big Data’, so what changed? How did Big Data eventually get sick, and then subsequently die? What went wrong? In a nutshell, it was beaten by its own success. It was made redundant by its own arrival.

Big Data could be seen as a hurricane on its way to a land that has never experienced such a phenomena before but is going to imminently – where people are forewarned well in advance that there is a disturbance in the weather, troubled times ahead, that the time is coming to baton down the hatches and seek cover before the incredible winds, rain and destruction arrive. The time for talking about it, for describing, defining it and giving explanation is before the storm hits. Once the storm hits, everybody is too busy making sure they come out on the other side in one piece.

A major change in how information was being generated and gathered was occurring that required attention and action on a number of fronts. Just as the village needed to be prepared for the high winds of the hurricane and in order to be convinced that it was coming, it was defined, described and explained to them; so was Big Data the catch-all solution to capture the revolution in data processing.

Information control

That job has been done. Mentioning Big Data is now pointless. The consequences of its arrival are already here in the flesh. Businesses are finding right now that they either have control of the information in their possession or they don’t. They are either benefitting from the intelligent analysis of the information that counts or they have been focusing on the least fruitful facts and figures – or maybe not mining from their data banks effectively at all.

Big Data was a hype word. It was a necessary one to galvanise people into action, to facilitate communication and sum up a range of phenomena that needed to be acknowledged – like the ‘swinging 60s’, but now the realities have already hit home the people who are really in the know are looking for the next change they need to predict. Big Data is rapidly becoming a ghost that is only mentioned by people who are not so savvy, or who are desperate to show understanding in order to sell a product or service.

Thanks for everything you did for us, Big Data. Good bye and God bless. May you rest in peace.

Image: Gerd Leonard

  • Kerry Butters

What’s your Data Governance Plan?
What’s your Data Governance Plan? 150 150 Kerry Butters

Big data is something that businesses are embracing in increased numbers, but not every business is prepared for the changes that accompany big data adoption. Information from a recent survey suggests that as many as 44% of businesses aren’t ready to implement data governance plans. 22% of these firms that don’t have a data policy have suggested that they have no plans to implement one.

These findings were released in a data governance survey from Rand Secure Data, which is a division of Rand Worldwide. The findings suggest that businesses simply aren’t prepared for the legacy of big data and it’s becoming apparent that many businesses are happy with the benefits of big data gathering, but are equally happy to ignore the dangers.

Businesses are aware of what needs to be done to safeguard its data but many seem loath to act or even address the problem. It seems that until there are consequences, many businesses simply won’t acknowledge or do the things that need to be done.

Here’s a quick list of things that businesses should be doing:

·         An enterprise-wide process for managing data archiving

·         Backup of system files and company servers

·         Promoting e-discover and incorporating it better

Many respondents in the survey said that their companies had yet to adopt any of the above. Whilst not every company is a culprit for poor business planning, a surprising number are. This is of course an area for concern and businesses need to develop good data governance plans and better prepare for the increase in big data usage.

There are a number of consequences to neglecting good data governance plans. According to the survey, the next two years are important and if companies don’t adopt better policies before that time expires then those companies could lose data, lose control of the tracking and gathering of data, and even risk potential lawsuits due to bad data governance.

The survey doesn’t just bring bad news however – there’s some hope in the sub regions of data management. Over 98% of respondents said that their company has some form of backup program for its data and 95% stated that their organisations backup all of its data on a consistent basis.

The role of e-discovery

E-discovery however is another area of concern for businesses and the danger of legal action due to mismanaged data stockpiles is high. According to the survey, over a third of respondents felt that the company they worked for would be unable to find and produce data when it was needed and the same participants also said that their organisation wouldn’t be able to prove the voracity of its data in the event of legal action.

These are areas that need addressing and a business shouldn’t feel that by ignoring the potential dangers of big data usage the consequences are negated. The potential for error obviously increases the longer that good data governance plans aren’t implemented. It’s not only forward thinking, but completely necessary and businesses shouldn’t shirk their obligations in favour of easy rewards.

Predictive coding

A lot of businesses are avoiding implementing predictive coding – even when this can save time and greatly increase productivity. Companies need to realise that time saved now is not greater than the time and hassle saved in the future by early implementation. A machine-learning e-discovery technology could almost negate human input and automatically determine how and where documents will be classified. This type of time-saving technology will also help in terms of making the network and its data much more readily searchable and it’ll increase the working output of employees.

Although e-discovery software is easy to obtain and implement, predictive coding is used by only 14% of those surveyed. Perhaps more telling is the fact that 33% said that they had never encountered e-discovery or predictive coding before. This suggests that e-discovery is an area that needs exploring and businesses need to be better educated on what it is and how they can use it to its advantage.

In terms of what businesses can do to become better prepared for big data through good governance plans, the actions required are pretty simple. Executives need to participate and help to devise data governance policy that benefits and safeguards the business. The involvement of executives and company wide policies mean that a business is three times less likely to lose its data or run the risk of a data audit failure.

The survey has four recommendations for businesses looking to adopt data governance plans.

1.      Organisations need to adopt a formal data governance plan or reassess the current plan. No policy will ever be faultless and of course there will always be exceptions but corporate entities need to work on and develop protective measures to ensure that the business gets the valuable data it needs safely.

2.      Use your organisation and solicit as much input as you can from your employees. They’re working with the data on a daily basis and they’re likely to have a good idea of what needs to change and develop.

3.      Make sure that any data governance policies are in keeping with your organisation’s legal requirements. There are many different types of data, each with different retention rates and you need to know how to meet those requirements.

4.      Be on the look out for new technology that your business could utilise to its benefit. Big data is growing and becoming ever more saturated and data governance should be reliable, scalable, and of course efficient. New technologies will arrive that can make it easier for your data governance goals to be met.

Many businesses are not doing what’s required of them when it comes to data governance. Businesses need to realise the dangers of not adopting new policies and understand that scrimping now will not save them in the future.

  • Simon Randall

[INFOGRAPHIC] IT Skills Gaps
[INFOGRAPHIC] IT Skills Gaps 150 150 Simon Randall

Technology is advancing at an incredibly fast rate to the point that many of those in business feel that they can’t keep up. This poses a big challenge to enterprises, both large and small, as they work to stay up-to-date with rapidly evolving technology that helps to make business processes easier and more automated.

Where this challenge is most pronounced is in the work force. As the infographic shows, a group of three hundred IT leaders in the U.S. were polled and 63% of them projected that the impending IT skills gap would have a negative impact on their business.

Preparing for the future

Whilst many businesses have made plans for the future and the hiring of new IT talent for their officers, over a quarter of the businesses polled did not have a plan in place to improve their work force through new hires. Perhaps more alarmingly, 60% of these businesses don’t have a plan to retrain their current work force in new IT techniques and technologies.

More than half of these businesses have conducted research to identify the future needs of their workforces however, and hopefully that will see those numbers drop as they begin to plan for their future. Only a little more than 20% of businesses feel poorly prepared as a result of their current research.

Short-term planning

Those businesses that do plan for the future, place an emphasis on planning for the next 1-3 years. Whilst this allows them to respond to problems in an agile manner, it doesn’t do much in terms of providing a long-term solution. Nowhere is this more obvious than in the statistic that 78% of IT leaders believe that a flexible work force (that is, a workforce made up of temporary, contractual or independent workers) is very important to them if they wish to achieve their current goals. 

Flexible work forces are often cheaper in short run, but in the long term businesses may end up paying a lot more for these flexible workers than they would have for directly employed workers who received the correct IT training. As it stands over 30% of the total IT workforce is made up of flexible workers, and that number is most likely going to rise unless businesses begin training in-house employees.

Be prepared

Perhaps the biggest change which businesses could make to slow the growing skills gap would be to keep their IT leaders alerted to any major changes in business practice, or any upcoming initiatives so that they can prepare their workforce more effectively. As it stands, only 25% of leaders are always alerted to these changes, with 74% receiving little to no warning ahead of time.

The four areas that most demand IT skills currently are:

·         Security

·         The Cloud

·         Business intelligence

·         Mobile Technologies

All of which are growing sectors that will likely make up a large chunk of the future economy. If they want to stay in touch, businesses are going to have to begin to consider training their in house staff up with new systems and technology, as well as keeping their IT managers more in the loop with upcoming events and clients for the company.

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