Managing data challenges with business intelligence

Business intelligence tools can help get a handle on big data.

There's no arguing the benefits that big data and business intelligence solutions offer for businesses in the digital era. The ability to conduct analysis on every granular detail of an organisation is opening up new avenues for productivity, efficiency and customer service improvement.

That said, there's a reason they call it 'big' data. The sheer amount of information flooding into businesses can be intimidating for individuals who are ill-equipped to handle it, and with increasing connectivity of devices from the internet of things (IoT), the scale is only likely to continue growing.

The sheer amount of information flooding into businesses can be intimidating.

The challenge of big data can be effectively summarised in the so-called Three Vs of Big Data – volume, velocity and variety – and understanding each one is necessary for businesses looking to capitalise on analysis.

Turn up the volume

This aspect is where big data mostly takes its name from, and the one that is the easiest to understand. To do so, let's look at a few figures.

According to IDC, in 2013 the total amount of data that existed was 4.4 zettabytes or, more colloquially, 4.4 billion terabytes. The data universe is growing at such a rate, however, that by 2020 it is predicted that the world will be home to somewhere in the region of 44 zettabytes – ten times the amount that existed just seven years earlier.

Obviously, no single organisation will ever be expected to analyse all of that information, but it gives an idea of just how quickly the landscape is growing. Whatever the amount of data your business is dealing with currently, are you confident that in just 3-4 years time you will be capable of managing 10 times as much? And are your existing business intelligence solutions up to the task of deriving insight from such a wealth of information?

Is your business intelligence equipped to handle data from new sources?Is your business intelligence equipped to handle data from new sources?

Variety is the spice of life

The second challenge of big data is variety, tied closely to innovation and how digital technology is being incorporated into more and more aspects of our lives. Even as little as 10 years ago, the data landscape was much simpler, with information being mostly contained to computers and peripheral devices, and software solutions such as email or digital photography.

The pace of innovation,however, has led to almost innumerable sources of digital information. ZDNet notes that users are uploading almost one billion photos to Facebook every day, most commonly from smartphones and other digital devices. Wearable technology such as smartwatches and fitness trackers are constantly generating new data about our movements and activity. Even our homes are becoming connected, with smart light bulbs and appliances just two examples of new sources of data.

Digital technology is being incorporated into more and more aspects of our lives.

Every piece of content, regardless of its source, has data associated with it, and traditional analytics practices such as spreadsheets are now unable to keep up with the myriad ways in which information is created. It's therefore essential that you have the most modern tools and technical support to manage it all.

Variable velocity

Not only is all of this new data being created at an unprecedented scale from more sources than ever before, it's also happening with incredible speed. The aforementioned Facebook example is a good indication – over 10,000 images are being processed by the social media giant every second.

While there is some level of consistency to that data creation, with trending topics frequently capturing the social consciousness, the rate at which new information is being generated can also increase with little warning. Should an event in your industry suddenly be catapulted into the zeitgeist, there's no telling what the impact might be on your organisation.

As IoT technology becomes more prevalent and big data continues to escalate, the faster it will need to be collected and analysed. With volume, variety and velocity all putting pressure on organisations and their business intelligence solutions, working with expert consultants is crucial. 

Speak to AtoBI today about the best business intelligence tools to meet your needs. 

What's your vision for making the most of BI?

Clearly presenting data-driven ideas is important.

There's been a lot of discussion in recent years about the value of business intelligence – especially in healthcare, where gathering and analysing better information has the potential to save people's lives. In fact, according to a recent study from HIMSS Analytics, 93 per cent of companies in the health sector report that better BI and analytics are a top priority for them within the next 12 to 24 months.

What your company really needs is a reformed, data-driven culture. There are many ingredients that go into that.

Having BI as a priority is one thing, and it's laudable. However, it's quite another thing to actually have a detailed vision for achieving BI success. Drawing up a more intelligent business is a complicated challenge, and it requires a lot more than simply throwing money at it. Even with significant investment of funding and talent, there's no guarantee that a BI initiative will truly pay off.

What your company really needs is a reformed, data-driven culture. There are many ingredients that go into that.

A complete business architecture

There's a lot more that goes into healthcare BI success beyond just installing enterprise solutions and hitting the ground running. According to the Northern Territory Department of Health and Families, what your organisation really needs is a comprehensive vision for business intelligence, one that's focussed on building a complete architecture.

This process should begin with data capture and continue to include reporting, analysis and application of all the information you collect. You need to have people on your staff who boast an entire range of skills. Can your employees capably collect data and store it securely? Can they perform detailed analyses that reveal new truths about your business?

Part of what makes a business data initiative successful doesn't involve data at all – it relates more to communication and leadership skills. Analysing information is great, but it's all for naught unless you can present your findings in an engaging fashion and articulate your case for real business change based on your information. Data isn't the end result. Rather, it's merely one tool used to drive improvement.

What it takes to be competitive

No matter what line of work you're operating in, be it healthcare or anything else, you're surely not alone in the data revolution. There are bound to be other companies in your industry using business intelligence solutions to analyse and improve their operations. This being the case, you'll need to look hard for a competitive advantage that can set your organisation apart from its rivals.

You want good, clean data to be available around the clock.You want good, clean data to be available around the clock.

Research from Marvel Technologies indicates that BI managers should look for three main values that can distinguish their BI efforts – data availability, reliability and completeness. Availability means not just having access to data, but having it at all times and getting information that's current, not outdated. Reliability means being able to trust that your data is accurate and relevant to the task at hand. Finally, completeness is key because no organisation wants to act rashly based on insufficient or misleading information.

Solution consulting can guide your business

It's not easy to have available, reliable and complete data that can drive your company's success. Even if you purchase the right BI tools to assist in this mission, there are no guarantees it will work. You may decide along the way that your company needs to reach out and ask for help.

That help is exactly what we provide at A to BI. We have a unique approach to solution consulting that's based upon listening carefully to you and tailoring BI strategies to your specific needs. We have a wealth of experience with designing, prototyping, developing and implementing analytics solutions, and we're ready to do all of the above for your business. Contact us today and we'll get started.

What is MapR and why should you care?

MapR enables professionals to manage information from a central location.

MapR has made great strides in developing Hadoop for enterprises across the globe. The company's flagship Hadoop distribution enables both public and private organisations to collate and analyse information in real-time – a capability that's becoming essential with the rise of smart devices. 

In this article, we'll detail how MapR works and how it can impact your business. 

Note: If you don't know much about Hadoop, you'd be better off reading our "Hadoop: A guide for the average business professional" series first just to get some context.

Designed for the data centre

According to the company's website, the MapR Converged Data Platform is an enterprise data product that uses Hadoop and Spark, offering high availability. The software delivers immediate node-based recovery, a lower total cost of ownership and support for organisations that require constant access to information.

In case you're not familiar with Spark, it's a general processing engine that runs across Hadoop clusters through YARN. The system is engineered to execute batch processing, interactive queries and machine learning throughout Hadoop-based environments.

MapR's key capability lies in running multiple Hadoop applications over one machine cluster. Many Hadoop distributions (distros) need to run in separate clusters. In contrast, MapR enables companies to run operational and analytic systems over one server array, thereby decreasing data management costs.

Through MapR, administrators can exercise central control over Hive, HBase and Drill.

Available, open and protected

MapR's accessibility complements its converged platform. Essentially, administrators can exercise control over technologies such as Hive, HBase or Drill through a single interface. In addition, users have the freedom to allocate workloads among clusters and pull data sets from any server array.

Using open source technologies provides MapR with a lot of support, but developers subject the technology to rigorous quality assurance processes. However, the company makes a point to validate, test and reinforce Apache project updates before integrating them into its data platform. 

This focus on QA directly translates to high performance and data protection features. MapR uses mirroring, replication and point-in-time snapshots as recovery tools. The system enforces security through wire-level encryption, consistently auditing data permissions and authentication protocols through Kerberos or the Lightweight Directory Access Protocol.

You can probably see why we've taken an interest in this technology. The system uses not only the scalability of Hadoop, but also the defensive tools organisations need from enterprise solutions. We're pretty excited about what MapR will offer in the future and how we can help companies make the most of it. 

Hadoop: A guide for the average business professional – Part One

Hadoop is at the forefront of big data.

If you've ever entered 'big data' or 'business intelligence' (BI) into Google, there's a good chance you've come across websites that mention Hadoop.

How has this framework affected data analysis solution consulting? Better yet, what even is Hadoop? Sounds like the name of a stuffed animal. 

The big yellow elephant

Hadoop is an open source software library that enables users to process large information sets across machine clusters (i.e. groups of computers that act as one) by utilising basic programming models.

Ironically enough, Hadoop was named after co-founder Doug Cutting son's yellow stuffed elephant, according to Kevin Sitto and Marshall Presser's "Field Guide to Hadoop". Mr Cutting and co-founder Mike Cafarella started the project to crawl the web and index content, making the basis of a search engine.

The video from Intricity101 provides a rundown of Hadoop's history:

The framework is distributed under the Apache License 2.0, which allows organisations and professionals to: 

  • Download Hadoop or any of its associated projects for personal and commercial uses, as well as for any purposes related to a company's internal operations.
  • Use any versions that an individual or company may create.

As per the Apache License, Hadoop is categorised as an open-source solution, meaning you don't have to pay to use and your developers can apply adjustments to the code based on your BI needs. 

How does it impact my operations?

OK, you can process data across multiple computers, so what? MapR, which develops, distributes and supports Hadoop, described two key technologies: the Hadoop Distributed File System (HDFS) and Hadoop YARN.

HDFS is lauded for delivering scalable data storage. According to MapR, the solution stores data across multiple computers connected to create a single cluster. (A cluster is exactly what it sounds like: A group of computers configured to act as one piece of hardware.) This makes it easier to process information in parallel across all machines. The result is cost-effectiveness: Instead of spending thousands or tens of thousands of dollars maintaining a terabyte of information, HDFS and its associated technologies keep expenses to three figures. 

Alright, so HDFS can store a lot of data. How are you going to process it? That's where YARN comes into play. This program delivers the operational oversight Hadoop needs to operate securely and optimally. In addition, it allows enterprise solutions to access HDFS to process different data simultaneously. YARN essentially gives HDFS the mobility enterprises need. 

If you've made it to the end, you have a pretty good idea of what Hadoop does. In Part Two, we'll discuss how Hadoop impacts big data and BI projects.