4 reasons why big data isn’t coming for your job

One of the key misconceptions about big data and machine learning is that algorithms will replace human data scientists. There is something of a debate around whether a team of analysts is better or worse than a well produced algorithm, but the truth is that the most effective way to prise actionable insights from your data is to make the most of both. While a machine eliminates human error, a human is vital for contextualising and creating a narrative from the insights, as well as working around the limitations of the data.

It only makes sense that CTOs and other decision makers should utilise the best aspects of machine and human performance. While they both have extremely strong points, there are drawbacks to both. Humans are a limited resource, as data scientists are in hot demand right now. Further, we can only process so much information at a time. On the other hand, big data without human input is still essentially just raw data. Let's explore why humans are still critical to leveraging useful insights from big data.

1) Humans understand the difference between correlation and causation

Just because two variables appear to be linked does not mean that they are.

Here's an interesting statistic. Between 2001 and 2009, per capita consumption of mozzarella cheese rose at almost the exact same rate that civil engineering PhDs were awarded. Is it because qualified civil engineers are known to eat stretchy cheeses exclusively? Is it because the cheese industry is handing out scholarships? Or is it simply a random coincidence?

Just because two variables appear to be linked does not mean that they are. You've heard the phrase "correlation does not imply causation." It might sound obvious because there is a clear lack of causation between mozzarella sales and civil engineering doctoral graduates (or vice versa), but when every metric you are reporting through your big data analysis tools relates to your business, it's often difficult to distinguish correlation from causation. A human consultant can help you differentiate the substantial relationships from the meaningless.

The difference between correlation and causation in big data.Is mozzarella the key to completing a PhD in civil engineering? No, no it isn't.

2) It takes a human to ask the right questions

When we say "ask the right questions" we mean identifying the right parameters and constraints for your analysis. A useful simile here would be asking a toddler what they want to have for dinner. They will likely reply "ice-cream", "candy", or "cake" and of course they did, because you didn't set realistic parameters for the question. All of the toddler's responses are legitimate answers to your query, but they don't get you any closer to a real, workable solution.

Such is the case with leveraging insights from big data. Asking the wrong questions usually means getting the wrong answer, but in some cases these incorrect responses can appear to be useful, actionable insights. Making decisions based on this information can be disastrous, The key lies in understanding how to use your analytics platform as well as how to engage with it to produce the information you desire. This is something that can be taught, but it must be taught by a human data scientist, without whom you might be left with the enterprise equivalent of a toddler in a sugar-rage.

Why is it essential to ask your analytics tool the "right" questions?Asking the wrong question almost guarantees you'll get the wrong answer.

3) Imprecise data collection leads to imprecise results

There are a few ways in which the data you've collected can be biased. This is particularly true in the case of user-data. In your marketing department, you might have metrics to collect data from those who have engaged with your website, ads, or other digital properties. The issue here is that this information doesn't represent your target demographic as a whole, but only those who have already engaged with you. Subsequently, analysis of this data will relate to your users only, not necessarily your target market as a whole.

A human analyst understands the limitations of data collection.

In the healthcare sector, you may be using patient records to help you provide a greater level of care. Since there is no centralised medical record database, anything you collect from the archives you can access will be limited. Whether this data covers a geographic area or a particular kind of ailment, the data set could be either too small for meaningful insight or too specified to be of much use on a larger scale.

A human analyst understands the limitations of your data as well as collection methods, helping you make more informed decisions based on the information that is available. Note that biased data can still be useful, as long as the bias is acknowledged and, as previously discussed, you're asking the right questions.

4) Humans provide all-important context

There's often talk of "creating a narrative" with data insights, and this is very much a skill that, at this point, belongs to human analysts alone. While visual analytics platforms can deliver a huge amount of value and simplify difficult relationships in order to inform business practice, a meaningful interpretation and simplistic explanation are what turns this information into good, workable decisions.

AtoBI can not only help you implement the right solutions, we can make sure your human staff have everything they need to get the most out of your data analysis tools. For more information, get in contact with us today.

What is machine learning and how is it used?

Machine learning is a term that's used frequently in the tech world but for some, it's still a source of confusion. Of course, we're all familiar with the concept of AI, and that's what machine learning seems to be describing, however they aren't interchangeable. Machine learning isn't so much about your smart fridge becoming self aware and asking difficult existential questions – It's more easily thought of as the algorithm that allows your preferred media streaming platform to learn what you like and make smart recommendations.

Systems built around machine learning have created massive changes in the digital world and this technology is helping facilitate positive change our analog lives as well. Let's take a look at what machine learning is, how it works (we'll keep it simple, don't worry) and how it's already making waves.

What is machine learning?

Machine learning describes algorithms developed to process and apply complex calculations to large sets of data, detect patterns and "learn" without being explicitly programmed. While such algorithms have existed for a while, the reason they are becoming increasingly prevalent in big data and analytics is because we've developed the tools to let these algorithms do a lot more, a lot faster. Big data guru and best-selling author Bernard Marr describes it in very simple terms, which we'll paraphrase here:

Think of your data set as a huge number of images. A considerable number of these images are of cats, and each image is labeled either "a cat" or "not a cat". The algorithm analyses each image and determines that all photos labeled "a cat" have similar characteristics. The algorithm has essentially learned what a cat looks like. From here, you could feed the algorithm images and ask it to identify which ones are of cats.

Machine learning: If an algorithm can learn what a cat looks like, what else can it do?This one is definitely a cat.

The key difference between AI and machine learning

The concept of artificial intelligence is over 3,000 years old.

In order to differentiate these two terms, we first need to look briefly at what artificial intelligence is. This concept is over 3,000 years old – artificial beings have been present in myths and legends dating back to the Indian philosophy of Charvaka circa 1500 BC, though the concept has existed in its current form since the birth of computer science in the early 20th century. At its most basic level, artificial intelligence describes a computer capable of mimicking the human decision making process.

Modern AI development generally falls into two areas. The first is applied AI, in which a system is designed to undertake a particular task. For example, this might be AI designed to react to real-time environmental data in Google's self-driving car. The second area is generalised AI, where a system is designed to be theoretically capable of handling any task.

An example of this would be AI research firm Deep Mind's "Alpha Go" algorithm, which beat the best living human Go player (for some context, Go is a 2,500 year old abstract strategy board game that's far more complex than chess – there are famously more possible board configurations than there are atoms in the visible universe). So, while applied AI reacts, generalised AI preemptively strategises.

Machine learning is a subset of general AI, and it was born from researchers experimenting to see whether computers could learn from patterns in data. The definition of AI might encompass what we know as machine learning, however machine learning only describes a fraction of what defines AI.

What's the difference between AI and machine learning?Machine learning doesn't refer to a machine capable of mimicking the human thought process, but such a machine would (will?) be built on similar tech.

Where is machine learning applied?

We've mentioned self-driving cars and algorithms that recommend video or audio media based on your history, so we'll skip over those for now.

  • Healthcare

Machine learning in healthcare is one of the most meaningful and profoundly game changing applications to date. Analysis of medical data is already being used to detect the early stages of breast cancer, and to reduce the incidence of avoidable hospitalisation for patients with diabetes. Additionally, it's allowed us to identify tuberculosis in chest x-rays and to gain a better understanding of risk factors for disease in large populations.

  • Security

From identifying malware variants to predicting security breaches, machine learning is helping us to stay protected in an age where cybercrime is one of the greatest threats to businesses. Not limited to digital security, algorithms using image recognition can help identify red flags that human personnel might miss in screening processes at airports, stadiums and other public places.

  • Marketing

With a dramatic increase in the revenues for collecting customer data, machine learning has helped us to refine marketing strategies to be more personalised. We're able to make smart recommendations of products, custom tailored offers, specialised newsletters and accurately targeted ads, all of which shepherd customers towards a sale. It's difficult to engage customers without knowing what they want, but with machine learning we don't have to.

  • Financial services

Banks, investment platforms and other financial institutions can use machine learning technology to gather valuable insights from data which can identify investment opportunities and inform investors when to trade. The speed with which these insights can be generated is responsible for these companies making smart decisions that regular old humans didn't have the processing power for. From establishing more people-friendly solutions to stock market predictions, machine learning is of great benefit in this area.

  • Search engines

You may or may not have noticed that search engines are a lot better than they used to be. This is because machine learning has helped platforms such as Google better understand how we interact with search results. This includes watching how users respond to results to provide a more personalised service, as well as determining which websites are best answering common queries and ranking them in order of usefulness. Machine learning is helping us get the information we need, quicker. Businesses can look at search engine algorithms and tailor their content to perform better in search.

Are you ready to find out what machine learning processes can do for your business? Get in touch with AtoBI today.

The wide reach of big data (and how it’s affecting your life)

Big data is all about combining and contrasting large data sets. You're probably used to the term being thrown around in the context of business, and it's true that the collection and interpretation of data can have extremely positive results for businesses looking to optimise their service offerings. But big data reaches further than that, much further. Our capabilities for leveraging insights from data are so much more robust in 2018 that big data is set to affect our lives in ways we never expected just a few short years ago.

Here are three ways big data is influencing the world around you, that you may never have considered.

How is big data making sports more interesting? Big data can assist sports teams in improving performance, recruiting better players and boosting fan engagement.

1. Improving performance of sports teams

While the success of a sports team once may have lived or died based on the intuition and experience of their coach, it's now possible to collect data on almost every aspect of any given sport. This can, in turn, be used to pinpoint areas for improvement, from recruitment to individual performance to fan engagement. But how is the data collected, and what can be learned from processing it?

Data gathered from on-field performance can pinpoint areas for improvement, from recruitment to individual performance to fan engagement.

Let's start with video analytics. UK Premier League soccer team Arsenal recently undertook a multi-million dollar project to begin gathering and analysing data. Part of the project was installing eight cameras around the stadium. These are set to record 10 data points per second, per player, totalling 1.4 million data points for every game. Data can be filtered down to address key areas of concern, for example, you could easily opt to view "all unsuccessful passes by Mesut Ozil" to find out where the player in question needs to work on his game.

Big data informing athletes is not limited to just video analytics though. An Irish tech startup recently launched a multifaceted talent identification platform capable of analysing physical, mental and social factors to help teams make better recruitment decisions. As more sports teams across the globe adopt this kind of technology, following your favourite team will become a lot more exciting, as the "underdog" teams will have the same developmental resources as the major players.

How does big data contribute to the sciences?Where more data leads to better insight, big data opens a world of possibilities for scientists.

2. Expanding the capabilities of scientific research

When it comes to scientific research, qualitative data is all important. However, in many cases, the more data gathered from experiments, the more accurate the result. Tulane University in New Orleans, Louisiana, have recently built an HPC cluster (a high performance computer built on multiple independent processors) to allow analysis of huge data sets. Not only has this allowed them to conduct more meaningful research in the fields of oncology, nanotechnology and quantum mechanics, it's also allowed them to branch into the areas of epigenetics, cytometry and the mapping of the human brain.

Big data tools have allowed CERN to whittle 40TB down to a single gigabyte of information.

You might already be familiar with the large hadron collider (LHC) at CERN in Geneva, Switzerland. This is the particle accelerator that many people theorised would create a black hole here on Earth (you may have guessed, but it didn't lead to much other than a Nobel Prize for the research team). The LHC creates approximately 40 terabytes of data per second.

Big data tools have allowed them to whittle this down to around a single gigabyte per second, which makes for far easier analysis of the critical information hidden within these data sets. If that doesn't impress you, consider this: The 2012 experiment confirmed the existence of the long rumoured "God particle," the last discovery needed to confirm that The Standard Model of Physics (you know, the thing that dictates how the entire universe works) is accurate.

Can big data make our cities more livable? Big data can improve transportation and resource management in our largest cities.

3. Supporting the development of smarter cities

It's certainly no secret that much of the early development of cities and urban centres weren't exactly focusing on long term sustainability. What we lacked in foresight then is coming to a head now. Many urban planners and public infrastructure professionals are looking for ways to make cities both more liveable and less environmentally taxing. So how is big data supporting the development of smarter cities?

The DSSO system helps operators adjust traffic signals to help keep the flow of vehicles moving.

The first factor, and perhaps most immediately gratifying, is the way that transportation is being improved. Utilising RFID tagged vehicles, GPS devices and smart sensors built into the roads, it'll be possible to reroute and clear traffic in congested areas. The city of Lyon, France has already integrated such a system. Dubbed DSSO, the system helps operators adjust traffic signals to help keep the flow of vehicles moving.

The second factor relates to energy and utility management. Monitoring the movement and waste volume of water could help identify opportunities for better resource management – a concept that has huge implications for cities experiencing drought. Tracking things like footfall in heavily populated areas could have a positive effect on how much electricity is used, since cities will be able to dim or cut lighting in public spaces when no-one is around. Seattle, Washington is currently looking into these kinds of applications to reduce their energy use by 25 per cent.

How can big data be applied to improve the world? Big data isn't merely an intangible set of information, it's a tool for bettering our world.

In many cases, big data is playing "the long game" in that what we are learning today will greatly inform the world of tomorrow. While we are in truth just beginning to understand the ways that big data can change our world, the progress we have made already is astounding. Being able to gather huge amounts of information in shorter time means we can act quicker, and implement meaningful change on truly grand scales. Big data isn't just for businesses, it's for everyone, and the reach is limited only by our imaginations.

For more information, get in contact with AtoBI today

How can we improve the state of Business Intelligence reporting?

Reporting on data analysis isn't new, but the process has changed dramatically in the last few years due to a number of factors. We now have a huge number of channels from which to gather data, and as a result we can collect this data in amounts previously unheard of. Further, we also have greater access to extremely powerful tools for housing, accessing and analysing data.

However, advances in technology doesn't necessarily mean that it's all smooth sailing. In a recent report from Narrative Science, the 2018 State of Business Intelligence Reporting, it was outlined that 62 per cent of decision makers in businesses of all sizes found that the reports they received lacked context. The pain points these professionals face is that reports often described the "what" but not the "why". Therefore, the issue is that they're implementing useful tools, but they are either not the right tools in context or not being utilised to their full potential.

Law of the instrument

If we take a step back and get theoretical for a moment, consider the law of the instrument. American psychologist Abraham Maslow once said "I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail." The statement itself is a representation of a cognitive bias to rely, beyond logic, on a tool that's familiar. This could very much describe the current approach to adoption of business intelligence platforms. 

How can you find better tools for reporting?Regardless of your industry, we are fairly certain this image does not accurately represent the issues your organisation faces.

The best way to address the shortfalls of your reporting process is to reassess what's in your toolbox.

The systems you have in place at present may have added some value to your business, however, the statistics show that the majority of enterprise decision makers find their 

reporting processes lacking. Those professionals should then ask themselves: Am I using the right tool?

Of course, we know that not all tools are equal and that while some are adaptable, others are only useful for a single purpose. The best way to address the shortfalls of current reporting processes is through reassessing what's in your toolbox. AtoBI are here to aid you with this – finding the business intelligence solution that addresses the unique needs and specific requirements of your business, regardless of industry. In doing so, you can give every member of your organisation the best tools with which to do their job. Here's how the key stakeholders can benefit.

Report authors

The report from Narrative Science outlined that analysts – those who are producing the reports – lack the time needed to undertake more comprehensive data analysis. According to the report, 61 per cent of analysts said they spend more than half their time performing manual reporting tasks. The number one stated reason for this was accessing the data needed for reports. Often, the data comes from elsewhere in the organisation, and collating it can be a time consuming process. The result of this is that analysts have much less time to write reports in a way that is both meaningful and simple enough to be understood by the intended recipient.

With more centralised data and automated collection methods, analysts would be able to gather the data they need with greater speed and also have the time to dig deep into the numbers to generate more useful insights.

Business Intelligence administrators

In charge of the business intelligence infrastructure of the organisation, administrators have a lot to juggle. This role encompasses the selection and installation of business intelligence tools, connecting the platform to multiple data sources and auditing systems to make sure they align with company goals – among many other things. When it comes to improving reports, administrators have the same priorities as analysts – they should be a lot more in depth, covering more ground with clearer takeaways, and reports also need to be presented in a way that's digestible for the less technically inclined, and finally, to be able to do so in shorter time.

While administrators could be considered the gatekeepers of the analytics tools, they themselves face limitations imposed by less robust or flexible platforms. Employing the right tools can not only allow administrators to support better reporting processes, it can reduce many unrelated pain points associated with the role as well. 

Business decision makers

The Narrative Science report showed that regardless of industry, decision makers require better explanations in the reports they receive. Numbers often replace the qualitative analysis they are looking for and as such, making sound decisions based on the data becomes more difficult. Virtually every business intelligence platform has been developed specifically to help managers and leaders make the decisions that are vital to an organisation's success. The attitude expressed in the Narrative Science report show that in many cases, platforms for business intelligence reporting aren't working the way they should.

If the hammer won't do the job, your business needs to find a tool that will.

And so we come back to the hammer and nails analogy. If the hammer won't do the job, your business needs to find a tool that will. That's why you should touch base with AtoBI, as we can assist you in finding the right business intelligence platform for your business as well as support you through its integration and train your key personnel in self-service use. To find out how you can improve the state of reporting in your organisation, get in contact today.

5 ways business intelligence and social media lead to success

Social media allows businesses a level of engagement with customers that in pre-social days were genuinely unheard of. With modern business intelligence software developed to handle dizzying amounts of data and generate insights of great value, businesses can now navigate their markets with extreme precision. 

Data gathered from social media platforms should inform the way your business presents itself online. Social platforms aren't simply there to gauge how many people you're reaching – they also provide valuable information on the customer experience. This information can in turn be used to optimise your customer's experience and develop a relevant and suitable voice for your business.

Here are five ways business intelligence and social media can work hand-in-hand to improve your business's reach, reputation and relationship with your customers.

1) Learn about what your customers are really after

View your social media follows as something of a small community. They'll likely appear to be a diverse group, but using the data collected from these communities can be used to identify trends and common threads between them. This can go a long way towards understanding your customers and what they expect from you. In turn, this information can be used to deliver a better service, grow the communities, and expand your business's reach. The unique insights and perspectives you can gain from a community of followers is essential to continued growth – helping you to make the best decisions possible.

2) Make decisions with reliable demographic data

When it comes to customer interactions, knowing who you're talking to is just as important as what you're saying.

While developing a community of followers goes a long way towards helping you establish your tone, social media and business intelligence can also assist you in finding out more about your customers. Understanding the demographics you're engaging with aids you in delivering a more tailored customer experience, but it also helps you to identify who you're not engaging with. This latter point can help you target new demographics who could benefit from your product or services. Understanding your demographic is also essential information for joining in the conversation. When it comes to customer interactions, knowing who you're talking to is just as important as what you're saying.

3) Use feedback to inform your content strategy

One of the greatest things about social media content is that you can clearly track the success of every piece of content you share. It's easy to discern what has worked and what hasn't, and this loops back to inform the content you'll produce in the future. For example, if you're producing videos that get a lot of likes and shares, but you're also producing longer form written pieces that are generally ignored, you'll know that video is the best medium for presenting information to your customers. Tracking engagement is also a great way to test the waters with different kinds of content. This means you can experiment with different things, and get a feel for what kinds of content work. Knowing how your followers will respond to certain content is the strongest foundation on which to build a content strategy. 

4) Spend your marketing budget more wisely

The previous three entries have all been about understanding your customers and how to engage with them – but your online community can fairly be interpreted as a snapshot of your larger customer base. Insights you generate from social media engagement can help you to determine targeted marketing strategies. Targeted marketing performs far better than traditional "wide-net" approaches, and since Facebook introduced it in the context of social media it's become one of the most popular marketing approaches of our time. Business intelligence helps to identify behaviour patterns on social media and these help you determine how to craft your marketing.

5) Monitor your competition

Competition is an ongoing struggle. There aren't many businesses operating today in a marketplace free of competition, and in order to compete, you need to know what your opposition is doing. Social media provides businesses with a platform not just to engage their own customers, but to observe how other companies are doing the same. This might identify shortfalls in your own strategy, or it may save you from attempting something that has fallen flat elsewhere. Further, checking in on the competition can provide valuable insights to your industry as a whole. The best part of all this? You can assess engagement of the competition without them knowing it, since the nature of social media means that numbers of comments, shares and likes are publicly accessible.

Business intelligence and social media working together provides extremely valuable insights that will assist you in generating leads, increasing revenue streams and raising brand awareness. In this day and age, a social media presence is absolutely essential to continued growth. To find out how you can get the most out of these tools, get in touch with AtoBI today.

What’s the difference between Business Intelligence and Visual Analytics?

You've probably read or heard quite a lot about both Business Intelligence and Visual Analytics in the last couple of years. A lot of the time, people use these terms interchangeably. This can be somewhat confusing, as although both technologies tend to be employed for the same reason – to provide valuable insights on which businesses can base future decisions – they are in fact separate ideas.

In order to clear up the confusion, AtoBI will outline the differences between Business Intelligence and Visual Analytics. We'll start with a definition of each.

What is the difference between Business Intelligence and Visual Analytics? Your stress-free explanation.Don't let the different terminology stress you out. We're here to help.

What is Business Intelligence?

Business Intelligence refers to technologies and strategies for the collection and analysis of business information. The purpose of Business Intelligence is to help professionals make better informed decisions about business practices. You wouldn't make a personal decision without first weighing up your values, your past experiences, and your goals for the future – and neither should your business.

While a business will obviously weigh up data of a different type, the reasoning remains: No decision should be rushed or made without careful consideration, and Business Intelligence brings together all the information that must be considered.

Typically, Business Intelligence has used by executives, managers and the analysts who support them, and the most common tools are the dashboard and reporting functions. Recently there has been a shift away from this as the market is moving towards self-service – wherein businesses are trained to generate the insights they need for their specific areas.

Gaining insights from Business Intelligence may have once been a job for the IT department, but most platforms are now designed with the end user in mind. This means that Business Intelligence platforms are increasingly flexible, offering support to a greater range of professionals who can use a robust toolkit to generate their own insights.

Of course, it's still of great importance that you choose the best platform for your business, integrate it in the right way, and have support on hand should you need it. This is where AtoBI comes in. Our team of professionals work with you to understand your business and what your goals are, so we can help you implement a solution that will help you meet them.

What is Business Intelligence?Business Intelligence refers to the technology used for generating performance-based insights.

What is Visual Analytics?

Visual Analytics is concerned with accurate and easily understandable visualisations of data. The purpose of Visual Analytics is to represent complex data sets in a digestible way. If you glance at a spreadsheet, you are unlikely to gain any immediate insight – you would need to sit down and carefully consider every column and row before any information could be extracted. A graph, on the other hand, takes only seconds to grasp – the data is condensed into its most simple iteration.

While Business Intelligence brings together historical, and in many cases, up-to the minute data to generate reports based on current operations, Visual Analytics explores data in different way to uncover patterns on which future predictions can be made.

With the use of data-mining algorithms, Visual Analytics is primarily automated. Through analytic reasoning, Visual Analytics platforms have the ability to discern complex relationships in information, be it qualitative or quantitative. Subsequently, these platforms can represent data that is otherwise unintelligible to a human, and through a process that is far from intuitive.

The capabilities of Visual Analytics tools can be manipulated in a number of ways to suit the end goal of the user. These tools are used by the same set of professionals as Business Intelligence tools, yet can provide projections based on more complex translations of data. AtoBI can help you find the Visual Analytics solution that you need. 

What is Visual Analytics?Visual Analytics helps you visualise projections from complex data sets.

What are the key differences?

The differences between Business Intelligence and Visual Analytics are often highlighted as being a matter of reporting vs. analysing, maintenance vs. growth, or understanding the past vs. looking to the future. While none of these are specifically wrong, here is our take:

While Business Intelligence and Visual Analytics do in many cases overlap, it's important to note that there is a distinction. Most simply, you could view Visual Analytics as a tool for Business Intelligence. Therefore, if Business Intelligence is "the roads" then Visual Analytics might be "the Highway" – a single road that can take you to multiple destinations.

The best Business Intelligence solution for your business will combine insights from the past with projections for the future.

Extending that metaphor, you could look at it like this: Your business is the car, travelling forward through time and space on your way to what you hope is success. Your odometer is Business Intelligence – it tells you exactly how far you've travelled since the last landmark. It doesn't mean you know where you're going, but this tool will help you assess the route you've taken up until now. Visual Analytics could then be considered as the "mile markers" on the side of the road – the signs that outline how far you have to go and which turns to take to make it to your next destination.

In our car analogy, the best way to move forward is to have both sets of information available – like what a GPS system might do. Similarly, the best Business Intelligence solution for your business will combine insights from the past with projections for the future – it's just a matter of finding out which software platform will make the best GPS for your business. 

AtoBI can help you navigate the BI marketplace to get to where you need to go.There is an easier way to get to where you need to go. AtoBI can show you.

AtoBI specialise in helping businesses integrate the best Visual Analytics and decision making solutions available. Not only can we help you make the right choice of platform, we can help you use it to the fullest extent of its capabilities so that you can direct your business into a bright future.

4 reasons CFOs should rethink the forecasting process

As a CFO you are likely to have a lot on your plate, and unfortunately the forecasting process is far from what anyone would consider streamlined. Essential data you're relying on can often be significantly delayed, or the traditional spreadsheet platforms you depend on for insights may not have the level of sophistication you require. The good news is that in 2018 you have more resources at your disposal than ever before. But why should you adopt a flexible, self-service platform for forecasting?

1) Traditional data collection is a time vacuum

Financial managers in businesses tend to lose around 18 hours per month manually updating and correcting spreadsheets

A platform that can instantly collate data from disparate departments is a huge drawcard for CFOs. As the means of data collection continue to advance, companies are building up huge repositories of data. Traditionally, much of this has been stored in siloed systems, and gathering it all into one place and format has been prohibitively time consuming – first waiting on reports from individual departments and then manually re-entering data into spreadsheets.

Financial managers in businesses tend to lose around 18 hours per month manually updating and correcting spreadsheets, according to a recent report on spreadsheet use from Ventana Research. While those hours will ultimately lead to accurate forecasts, the time itself does not represent any value to the business. Making use of a platform that automates the process of gathering data does more than save time. Because you can access deeper layers of data, you're freed up to perform multi-dimensional analysis, which in turn leads to better insights, and greater value to the business.

How can data consolidation lead to better financial forecasting?Digging through mountains of data doesn't have to cost you time and energy.

2) You need consolidation of actuals as well as historical data

While gathering data can be a headache, sifting through mountains of it for the right data can be even worse. There are many business intelligence and analytics tools out there that report on actuals, but when it comes to forecasting, actuals need to be compared back to budgets to effectively evaluate performance. This is what leads to informed decisions and accurate business forecasting. Without the ability to compare actuals to planning models, you're almost working blind. This is how mistakes are made.

By integrating an analytics platform that offers a 360-degree view of the whole business, the entire process is streamlined. Rather than relying on multiple incompatible tools and trying to fit them together, you can instead rely on a single tool that ties historical data with actuals in order to aid you in a more accurate financial forecast.

3) There are significant limitations to spreadsheets and data warehouses

Spreadsheets are still very much a useful tool for forecasting, and indeed, all types of data analysis, however they are limited when it comes to enterprise reporting. They weren't designed for extensive analysis or forecasting, and they weren't designed as data repositories either. When used as such, they can become a nightmare to navigate.

Fewer than 30 per cent of businesses built a data warehouse capable of meeting their needs.

Traditionally, data warehouses have been built, often at great cost, to store all the data gathered by formerly siloed departments. While data warehouses can work wonders when optimised through routinely cleaned and conformed data, a report from Gartner stated that fewer than 30 per cent of businesses built a data warehouse capable of meeting their needs.

CFOs need a tool that connects to disparate systems automatically, reducing the huge cost and lengthy build time of a data warehouse. This tool should allow users to work in a single platform with access to all systems, as well as providing storage capabilities to support it. 

Is there a BI platform that can out perform data warehouses for conforming data?Data warehouses are expensive to build, and they often don't meet the needs of the organisation.

4) Reliance on IT is never ideal

Even with business intelligence tools in place, many financial managers, and managers in all other areas of an enterprise, must rely on the IT department to produce reports. While IT departments often implement tools to help key personnel compile reports on their own, much of the time these tools are department specific and don't communicate with the rest of the business. In this sense, the solutions don't go far enough to offer a robust platform for CFOs.

Many traditional solutions don't go far enough to offer a robust platform for CFOs.

To combat this, many finance professionals will learn shortcuts and methodology for how to get what they need with the tools available. This doesn't have to be the case. Making the best of a bad situation is not a framework sustainable in the long term. You need business intelligence solutions that meet your needs rather than to try and adjust your needs to align with your existing systems.

There is a much better way to approach the forecasting process and that comes in the form of implementing the right business intelligence solution. AtoBI has partnered with many companies that are leading the way in the business intelligence and analytics space. We can not only help find the best platform for your business and needs, but we can assist in integration and can provide support as you learn how to leverage it to your advantage. If this article has hit close to home, then contact us today to find out how AtoBI can help you.

How can BI technology help healthcare providers service Australia’s ageing population?

Australia's population is ageing. Australian Treasury stats reveal the number of Aussies aged 65 and over is set to double by 2054-55. Because older people use health services more than younger demographics, increasing pressure is being placed on the healthcare sector to provide quality care.

Technology, and more specifically, business intelligence technology can help your healthcare organisation meet these challenges.

Australia's healthcare sector is under a significant burden as the result of our ageing population. Australia's population is getting older, and this is creating some difficulties for the healthcare sector.

A quick look into the challenges facing the aged care sector

By 2053, the Australian Institute of Health and Welfare (AIHW) estimates that 21 per cent of our population will be aged 65 or more – that's 8.3 million people.

75,000 baby boomers will be living with dementia by 2020.

People in this age bracket are higher users of health services. And we're witnessing an increase in the prevalence of lifestyle issues such as diabetes among this age group now, the AIHW points out. Mental health problems are also on the rise among older populations, with a Deloitte Access Economics report estimating that 75,000 baby boomers will be living with dementia by 2020, with the illness being the third largest source of health and residential care costs by 2030. 

The central challenges for the healthcare sector are how to deal with a growing aged population, and the impact of chronic disease among this demographic, while not compromising on quality of care.

How can business intelligence technology help address aged healthcare challenges?

Business intelligence isn't a miraculous solution to aged healthcare problems. However, used and interpreted in the right way, it can really help you get a clearer picture of where the biggest challenges are coming from, where it can cut costs and how it can improve processes. All of these things lead to an improvement in efficiency and quality of care. So, how can you use business intelligence for aged care in Australia?

Business intelligence is a means of harnessing the power of technology to collect, manipulate, interpret and present data that can help you make sense of your organisation. In the healthcare sector, business intelligence software such as Tableau, Power BI and Qlik can be used to give providers visibility over the care being delivered to patients, rostering and budgeting.

Business intelligence tools help give you better visibility on the workings of your aged care organisation.Struggling to get visibility over the everyday operations of your healthcare organisation? Business intelligence tools can help.

Here are some of the ways business intelligence tools can help you meet the challenges of an ageing population: 

  • Presentation: Business intelligence tools help you present large amounts of complex data in a more user-friendly way. Get to grips with the most important data that you actually need to keep an eye on, rather than constantly sifting through an enormous amount of material that doesn't tell you anything. Help key stakeholders better understand key information by delivering reports that showcase important data in a digestible way. 
  • Monitoring: Investing in business intelligence software can help you better keep track of how patients are using your services and find out where you're falling short and need to invest more resources. In this way, you can deliver a higher quality of care to your patients by understanding their needs and ensuring these are met.  
  • Visibility: Using business intelligence platforms in the right way can help you pinpoint where costs are being incurred, and where savings can be made. This allows you to devote more attention and resources to certain services or areas of your business to meet growing demand. Modelling and visual representation can also help you improve efficiency in your operations, rostering and other processes, by highlighting roadblocks. Identifying these enables you to take action so you can deliver the best care possible. 
  • Forecasting: By collecting, manipulating, interpreting and presenting particular data sets, business intelligence tools can help you with decision-making processes by forecasting for the future. You'll be able to anticipate problems before they become too dire, instead of responding to them as they arise. 

How do you implement business intelligence solutions for your healthcare organisation?

Technology can help improve aged care, but only if it's manipulated in the right way – in other words, in accordance with your unique goals. To implement and begin using business intelligence solutions for healthcare and begin improving the care you provide your patients, you need a team of experts who understand your objectives and needs and can show you how to use the software properly in order to achieve these. 

Get in touch with the business intelligence experts at AtoBI, who will work with you to ensure your goals are met.

What's the key to implementing BI at the enterprise level?

Getting started with business intelligence is a challenge for any company, but it's especially the case when you're dealing with a large enterprise and a lot of moving pieces. What's the best way to get a BI strategy off the ground at the enterprise level?

You'll need to allocate a lot of resources to getting the enterprise BI process right.

There's little doubt that this is more challenging than with a small or midsize business. You'll need to allocate a lot of resources to getting the process right, not to mention get a lot of people on board with your company's IT plans.

There's a lot of buzz in the business world today about the value of BI, but there's surprisingly little in the way of tangible advice – how can you get started? What does the optimal enterprise-level BI strategy look like?

Get strong support from the top down

Worried about the challenge of implementing enterprise solutions for BI? Your concerns are not unfounded, but you can overcome the challenge if you go in with a solid game plan. According to Forrester Research, the most important prerequisite is strong executive commitment. If the people at the top of the corporate ladder put their support behind business intelligence, that should trickle down.

Support comes in many forms. Obviously, you'll need money to pay employees and purchase software solutions, but it's not strictly financial. Your company's leadership can also help with communication, training and coordination of logistics to ensure BI success.

Empower your staff to achieve their goals

Among the consumer-facing businesses of today, there's been a movement toward being more "customer-centric." The idea is that people deserve to have more control over how they communicate with brands and personalise their own experiences.

Get your whole staff up to speed with the BI implementation process.Get your whole staff up to speed with the BI implementation process.

Why shouldn't business intelligence solutions be the same way? This is another key to enterprise implementation. If you treat business users like customers and make a pleasant experience a priority, you're more likely to see people stick with your BI solutions and use them efficiently. An empowered staff is a more productive one.

Get consultants to smooth everything along

To ensure a smooth start with business intelligence at the enterprise level, one positive step you can take is to get help in the form of solution consulting. By getting tailored services aimed specifically at improving your business, you can start off on the track to success.

We have many of the brightest minds in business consulting in Melbourne among our ranks. Talk to us today, and we'll help identify your greatest challenges and develop the solutions you need to overcome them.

3 of the latest trends to watch in business intelligence

There are plenty of business leaders out there who have already begun to invest in business intelligence (BI). After all, increasing interest in BI has been one of the most notable trends across the business world in recent years.

What's changed so far in 2016 and the early part of 2017 in the world of business intelligence?

But for many leaders who have been at it for a few years now, there's a growing sense of worry that their strategies are becoming outdated. What trends do they need to be aware of if they want to remain current? What's changed recently in the world of business intelligence?

Let's go over the latest business intelligence trends in Australia and how your company can adjust to them on the fly.

3 of the most notable trends

Curious about the latest business intelligence trends to watch as we enter 2017? You're in luck, because the most recent Gartner research has turned up a few key developments you should watch in 2017:

  1. The movement these days is towards "advanced analytics." In other words, companies are adopting enterprise solutions for gathering data faster and analysing it more efficiently than ever before. This helps them keep the budget lean and compete with business rivals at an entirely new level.
  2. Smart data discovery is a key point of emphasis. The current tools aren't always the best for clustering and linking data, or for finding correlations in complex data sets. That process is evolving.
  3. Another objective is to empower employees. Everything in technology today is moving toward self-service, and analytics is no different. The goal in 2017 will be to give every worker the tools they need to crunch the numbers themselves, quickly and independently.
Give employees the power to control their own BI destiny.

How your business can capitalise today

Knowing what the major business intelligence trends to watch are is only half the battle; the other half is actually capitalising on them by taking fast action at your place of business. This is now an imperative for everyone. PCMagazine correctly noted that we're seeing a democratisation of business intelligence solutions – companies of all sizes, in all industries, are getting involved.

That means you need to climb aboard the bandwagon, lest you risk being left behind. Fortunately, at AtoBI, we offer tailored consultancy services that should make that process easier. Talk to us today about how you can act upon the latest BI trends and bring your organisation up to speed. No matter what type of work you do, we can help you do it better.