For you, data is more than a bunch of numbers. Its stories waiting to be discovered, potential solutions to problems your organization is experiencing and, perhaps, a guide to future trends that can inform business decisions today. Thanks to the rapid advancement of technology, data is increasingly trackable and available for professionals to analyze and use to steer their organizations toward success.

If this excites you, you might consider earning a masters degree in business analytics. With a bit of research, you can find an online program that fits your goals, schedule and budget.

A masters in business analyticsis a graduate degree for people interested in synthesizing data to inform business strategies. Whether youre a career-changer or a business professional looking to advance, a masters in business analytics will introduce you to various tools and software needed to organize, interpret and present data.

Data comes in a range of forms, including numbers, text, audio and video, according to Dr. Tej Dhakar, a professor of management science at Southern New Hampshire University (SNHU), and its up to business analysts to draw insights from it.

We are witnessing a huge explosion of data creation, especially since the beginning of this century, Dhakar said. New data is being generated constantly by ubiquitous smartphones, emails, streaming, web surfing, social media, sensors, point-of-sale systems and many more devices and activities.

Business analytics courses will teach you how to navigate these different data types and draw meaningful conclusions from them.

Because business analytics careers rely so much on data analysis and general business knowledge, people with a background in business, math or statistics often gravitate toward it, said Jennifer Oquist, the director of new learning models at SNHU. They typically have the analytical tendencies needed to connect data to the big picture.

But, through practice, you can learn appropriate methods, tools and questions to ask that can help you develop an analytical mindset and approach to data. Generally speaking, anyone with basic quantitative, computer and analytical skills coupled with an interest in data analysis and willingness to learn can succeed in this program, Dhakar said. Add interpersonal and teamwork skills to the mix, and you will have what it takes to succeed in a career in business analytics.

Dhakar said there are three types of business analytics that a masters program will discuss:

Its also important that you develop business, technical and communication skillsbeyond data interpretation too. Students should learn business strategy and financial modeling, data visualization, analytics tools and techniques and how to communicate business insights to a variety of stakeholders, Oquist said.

A masters program should help you practice your communication skills because your job wont stop after the analysis is done. Once youve made sense of the information, youll need to share your findings with those involved. (Business analytics) involves storytelling with compelling narratives and appropriate data visualizations so that leaders can make better decisions, Dr. Candace Sleeman, technical program facilitator for business analytics at SNHU, said. According to Sleeman, developing project management, research, organizational and programming skills are also essential.

Some of the areas youll have an opportunity to focus on in a degree program are:

Since the profession is continually growing and changing, your degree should not just teach you how business analytics works today. It should also equip you with the skills to adapt and stay relevant in an ever-changing workforce, according to Sleeman.

Finding an online business analytics program that fits your life is important. Be sure to consider factors including affordability, timing, relevancy and faculty in your decision-making.

Additionally, looking at your short- and long-term career goalscan help you determine when you want to have your diploma in hand. Typical masters programs can take a couple of years to complete, but some are fast-tracked. You might be able to finish in just over 12 months, helping you achieve your goals in less time while also saving you money.

All business analytics programs are not created equal, Dhakar said. ... When selecting a masters program in business analytics, look for how closely the courses in the programs relate to analytics. Also, look for what skills you will acquire through the programs.

Working with data management software including Excel, Tableau, SAS and Microsoft Project are good indicators that your courses can help prepare you for conducting business analytics in the professional world, as will courses that spend time on languages such as SQL, JavaScript, Python and R, Dhakar said.

Programs that emphasize hands-on experiences allow you to put theory to practice even before you receive a diploma. Hands-on experiences using real-world data sets and business problems are the best preparation for the workplace, Oquist said. Some data sets SNHU students have explored, for example, include national organizations such as Multiple Sclerosis Society and Heroes for Hire as well as local nonprofits like City Year New Hampshire.

Finding programs that establish collaborative settings from the start is also beneficial. Working with others is not only reflective of real-world challenges, but it also develops a learning community that can help you succeed in and out of the classroom. Youre not on your own, Oquist said. You have classmates to work with on projects, to support and encourage you as you learn new skills and to network with as you gain new professional contacts.

Faculty should be industry veterans with recent experience, Oquist said. Not only should faculty know what they are talking about, but they should also be immersed in the career themselves. Thats because they can give you the most up-to-date information about a role that undergoes frequent changes as technology advances. They know what employers are likely looking for perhaps as employers themselves and can help you gain the knowledge and skills needed to pursue your goals.

With a masters degree in business analytics, you can be a valuable member of teams that focus on marketing, finance, human resources and other business operations. Whatever your interests are, theres likely an analyst position in both the public and private sectors that aligns well.

Quantitative skills and data literacy are increasingly important to roles that range from data management to financial modeling, social media analysis, surveys, skills mapping and recommendations to customers, Sleeman said.

Some job titles you might encounter in your search, as reported by the U.S. Bureau of Labor Statistics (BLS) and O*NET OnLine, include:

While most entry-level analyst positions require at least a bachelors degree, earning your masters may give you the knowledge, skills and authority needed to take you and your organization to the next level.

The demand for business analysts is growing and growing fast. As long as businesses ask hard, critical questions, there will be opportunities for talented business analysts to add value, Oquist said.

The projects you will work on as a business analytics student will help you grow your portfolio and demonstrate to employers that you have experience handling different challenges, ultimately helping you stand out as a job candidate.

(Earning the master's degree) indicates a deeper understanding of business problems and a more robust skill set that will enable advancement into a variety of roles, Sleeman said. "It enables consideration for more technical and quantitative roles and a competitive advantage in a rapidly changing marketplace.

Rebecca LeBoeuf 18 is a writer at Southern New Hampshire University. Connect with her on LinkedIn.

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How to Get an Affordable Business Analytics Master's Online - Southern New Hampshire University

The spread of COVID-19 has seen renewed interest in remote operating centres (ROCs) and how they can be better managed to maximise efficiencies and reduce the number of personnel required on a mine site at a time, according to MST Global.

Rio Tinto was one of the early adopters of ROCs, introducing the worlds first fully autonomous haul trucks at its Pilbara iron ore operations in 2008 followed by the launch of an automated hub in Perth, Western Australia, in June 2010, which controlled its rail systems, infrastructure facilities and port operations, 1,500 km away from site.

In July 2013, BHP followed suit, opening an automated ROC in Perth for its seven Pilbara mines. Today, all the major players globally have introduced similar ROCs to their operations.

As an underground mining technology provider, it has been an exciting time to be a part of the industry as we develop hardware and software solutions that help our mining partners through this digital transition, MST Global said.

In a recent report, McKinsey & Company confirmed MSTs observations, citing, in the middle of the COVID-19 pandemic, mining executives had shown a greater interest in ROCs to unlock further value for their operations.

As mining companies seek to mitigate the impact of the COVID-19 pandemic and act to safeguard employees, some have started to relocate around 15-20% of their on-site workforce by setting up control towers to facilitate remote working (especially for non-frontline roles like subject-matter experts), McKinsey & Company stated.

This is helping the industry develop more resilient, responsive and flexible operating models suited to an increasingly uncertain environment.

MST Global says it has seen this first-hand, with many existing and new clients reaching out to the mining tech provider to assist in streamlining their operations, and looking at ways to effectively increase remote work capabilities.

MST Global CEO, Haydn Roberts, said the discussion has also centred on the transition to a smart mine, where systems and processes in place on site work together to unlock greater value for ROCs.

COVID has really focused our minds on the importance of having enough bandwidth underground and adopting IoT and digitalisation strategies so we can have a smart mine where our sensors and video cameras are connected, and we can operate in a remote way, Roberts said.

Remote operation centres have become really key because of all those things, so thats driving the change.

Mining companies are admittedly still in the early stages of their digital transformation, continuously looking at ways to improve to deliver on objectives.

McKinsey & Company said: Some companies have implemented cloud-based systems that aggregate site data into a single data lake that can be accessed, analysed, and visualised for decision support, creating a room of screens; other companies manage and actively control plant automation systems, fleet management systems, and remote-controlled machines from the ROC.

The most sophisticated companies manage all these functions on a larger geographic scale, covering the value chain from end to end, optimising post-processed ore logistics and port facilities used by multiple mine sites within a region, with regional parts and supply warehouses monitored across multiple assets for supply-chain optimisation.

McKinsey said while the technology adoption was the easy part, its research revealed a common challenge: insufficient emphasis on and investment in developing a robust change-management strategy and subsequent implementation.

It highlighted the importance for leaders to set clear expectations of bottom-line impacts from ROCs to measure value and the need for a new decision-making structure to allow ROCs to reach their full potential.

Without a new mandate, a new way of working, and a new decision-making structure, the ROC staff will struggle to capture the frontline teams attention, the report stated. And, although the ROC is implemented and functional, it never reaches full potential for value. Without a conscious focus on organisation, a ROC can be counterproductive, creating redundant organisational structures.

It added careful consideration must also be given to data and systems reliability, location of primary physical storage infrastructure, back-up systems and having a robust cybersecurity approach to protect ROCs from potential threats.

These decisions can impact connectivity, bandwidth, and latency, each of which must be sufficient to enable the ROC to effectively control on-site operations in real-time: for example, adjustment of plant processing parameters or remote control of mobile equipment and process optimisation tools, such as machine-learning algorithms, it stated.

With the right technology foundation, the ROC can function as the analytical centre of excellence, setting data standards, creating and updating analytical optimisation models, building analytics capability and driving partnerships to co-develop solutions aligned with the new planning process for optimising site-level profit. Such actions can move the organisation toward new ways of thinking about hierarchy, decision rights, and ways of working.

There was also the issue of jobs, and how transitioning to autonomous operations and ROCs will impact workers on site.

MST Globals Roberts said from his experience, so far with ROCs, this wasnt something the industry should be too concerned about.

I know some people talk about a fear that were going to take people out of mining and people will lose jobs, he said. I actually see the exact opposite of that. I think it is going to bring more people together in more meaningful work, more productive relationships.

Well focus on things that will bring a new lease of life to mines. Yes, we will remove people out of harms way and perhaps machines, but the amount of upside there is to actually work with these solutions, from AI to big data analysis to automating and adopting more smart sensors, this is going to create a more interesting future for a lot of people.

Mining is not going away. We obviously have to adapt it and change it to these new technologies and solutions that we have available. The people that were bringing into this industry expect that.

MST Global concluded: At MST Global, we are proud to be helping our mining partners globally embrace the transition to the smart mine and ROCs no matter where they are at in their journey through our leading software and hardware solutions.

Our brand-new software platform HELIX helps underground miners create a complete digital ecosystem underground, connecting all their hardware and third-party integrations into one single platform that provides real-time data anywhere, anytime, on-site or thousands of kilometres away in a ROC.

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MST Global on the rise of remote operating centres in mining - International Mining

While many companies currently have Business Intelligence (BI) solutions, there is a real need to lean into an entirely new generation of analytics to truly get the value from their data. The advanced data analytics software available to organizations today gives users more control over their usage by leveraging Artificial Intelligence (AI) to maximize the breadth of insight brought to light.

Heres more on what features advanced analytics brings to the table and what enterprises need to know about getting the most from their data analytics endeavors.

Key Advanced Analytics Features

As Gartner defines, analytics must autonomously (or semi-autonomously) explore data using tools beyond the scope of traditional BI to be considered advanced. So, its useful to think of this tech in terms of a collection of cutting-edge features that transcend the limitations of legacy self-service BI.

AI data mining

Data mining involves scouring huge repositories of data, seeking out useful insights hidden within. In other words, its like mining for gold in a mountain of rock and dirt. AI algorithms can sift through huge quantities of data more quickly than human analysts can, which helps companies discover potentially useful patterns, relationships and occurrences humans can use to inform decisions. Side note: Rather than making analysts obsolete, AI data mining frees them from one of the more tedious and time-consuming manual aspects of their jobs so they can work on higher-level projects instead.

Machine learning

Machine learning goes hand in hand with AI data mining allowing algorithms to learn from data findings/results so as to automatically improve relevancy and accuracy over time. This facet of advanced analytics eliminates the need for data scientists to manually train algorithms.

Predictive analytics

The predictive component of advanced analytics utilizes past and present data to understand performance changes over time to forecast what should happen in the future. The speed at which advanced analytics can crunch numbers, provide real-time performance analytics and forecast gives enterprises a better idea of present and future outcomes particularly when you compare these capabilities with the limitations of the legacy BI outlined above.

Natural Language Generation (NLG)

Instead of just getting an alert that product X has gone up by 30%, youll get an alert its gone up by 30% because a marketing promotion is more effective than usual and seeing 50% more conversion than other campaigns.

Natural language processing (NLP)

In part, NLP allows users to query data by typing or speaking naturally, and ensures data visualization models come back understandable to human users. Not only does this feature make search-based analytics tools easier for people to use up front, but also aids with the interpretation of results. The ability to provide an intuitive search experience, as well as understandable results, plays a major role in motivating people to adopt analytics tools and in helping them beneficially understand and act upon their findings.Advanced analytics address the limitations of traditional BI and then some, with features like AI-driven data mining, machine learning, predictive capabilities, interactive graphs, and natural language processing. However, advanced analytics also needs to avoid falling into the same trap that befell BI AA needs to be accessible and usable by businesspeople, otherwise, the same bottlenecks that broke BI will break AA.

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What You Need to Know About Advanced Analytics - 1redDrop

Founders from 16 of the UK's leading AI startups and scaleups have revealed to Information Age the main pain points of running an AI business

There are a number of unique challenges that AI startup founders encounter when setting up and growing their business.

Running and growing an AI startup comes with its own unique set of challenges compared to other sectors, ranging from; talent acquisition, how to implement the technology, busting the AI hype around what it is and its capabilities, the accessibility of trusted or secure data and a lack of AI expertise in industries.

In the first article of a three part series focusing on what founders are doing to navigate the fast growth AI industry, Information Age spoke to the founders of some the UKs leading AI startups to understand the ten main pain points of running an AI business.

A major challenge for any growing AI startup, as well other technology-focused roles (like cyber security), is talent acquisition. In these industries there is often a talent shortfall compared to the number of jobs available.

Miriam Cha, co-founder and COO of Rahko a quantum machine learning company focused on the discovery of new drugs and materials, explains that attracting the best people from relatively scarce talent pools is a major hurdle.

As an AI startup you compete with much larger companies who have much deeper pockets than you so you need to offer something more important than salary. At Rahko, we have a friendly, fun team, but far and away the most attractive thing about joining us is how absolutely cutting edge our work is if our work was boring we would have a very hard time convincing anyone to join us, she says.

Darko Matovski, CEO and co-founder of causaLens an AI platform that helps businesses improve KPIs, agrees that asan emerging discipline, attracting top people with a diverse skill set to ensure we not only develop our scientific capabilities and products, but also provide the best service to our customers is a major paint point.

There is a high demand for AI expertise, so hiring in this sector is very competitive, he adds.

However, Matovski suggests AI startups should view this as an opportunity. The best talent will only want to work in an environment with an altruistic working culture and shared goals.

Chris Ganje, CEO and founder at AMPLYFI which uses AI analytics to mitigate emerging threats as they happen, suggests the challenge lies in the range of skills required at an AI startup.

He says: It is not enough to lock some AI experts in a room. Engineering, data mining, user experience, design, digital marketing a range of skills are required in the mix to really achieve a meaningful solution and value.

Safe Hammad, CTO and co-founder at Arctic Shores which improves the hiring process through behaviour-based assessment, agrees and says he has struggled to hire software engineers because theyre in such high demand.

However, he goes one step further. Its not enough to find talented people were looking for those who truly align with our values too, he adds.

The introduction of AI into a business strategy or product is still at a relatively early stage. And, while the potential benefits are clear speeding up process or leveraging smart data for customer success the challenge is pinpointing precisely how, where and why youre going to implement the technology for optimum results, according to Dr Alex Young, founder and CEO at Virti the enterprise AI learning solution.

He advises businesses must be clear on what outcomes they want to achieve, before incorporating AI into a business strategy or product.

Ky Nichol, CEO at Cutover the work orchestration and observability platform, explains that this AI implementation pain point comes from some parts of a business that dont have ahealthy consideration of human/machine collaboration, and so adoption of automation can often be tricky. It remains fairly well understood by technical teams, but as it moves across organisational requirements, there can be knowledge gaps between adoption and implementation.

He continues: As organisations design strategies to unlock this potential and complement automation with existing technology infrastructure, there will always be challenges to leverage existing investments too. The challenge now is to join up tools and build processes and flows of work across them.

Ganje from AMPLYFI suggests that some companies and their infrastructure is simply not ready for AI presenting another challenge for AI startups.

He says there is a need for mature digital leadership to really exploit the benefits of AI in an organisation[but] some leaders and companies are just not ready to use machines to enhance their decision making.

As an extension to the above, one of the main pain points for AI startups in delivering their solution to market is a lack of AIexpertise in the many industries that are trying to implement AI.

Because AI has clear limitations that different industries are not familiar with, they view it as this all mighty capability that you can just turn on. The problems come in as they are not familiar with the foundations needed to run AI properly in their business. They are also not set up correctly to adapt to these requirements, explains Ofri Ben-Porat, CEO at Edgify a company that trains deep learning models at the edge.

Matthew Hodgson, CEO and founder of Mosaic Smart Data a startup that delivers the insight and real-time intelligence for the financial industry, highlights the irony that data resemblesboth the biggest challenge and most integral component to deploying AI effectively for any startup or business.

Looking specifically at financial institutions, Hodgson says that they must ensure that their data foundations are fit for purpose.

Data is the raw material of our industry, and without it, the benefits and potential of AI are stunted and capped before the system even gets switched on. Many financial institutions already sit atop mountains of their own data in addition to buying more from vendors yet they do not have the time, the resources or the staff expertise to sift through it, Hodgson explains.

Dr Richard Ahlfeld, founder and CEO at Monolith AI a startup that builds new machine learning software to help engineers to improve the product development process, echoes this view.

He says: Any pain points tend to boil down to the data: getting the data, ensuring data security, making sure that you can trust the data.

Theres no standardisation of what makes data valuable across the industry either, and not all engineers follow the same protocols and practices. For example, deciding what data to keep can be tricky as its hard to anticipate what might or might not be useful to have in the future. Even saving data from failed ventures (a practice which is often overlooked) can have its value, as it acts as a reference for future experiments.

While true artificial intelligence is some way off, businesses are taking advantage of intelligent automation, like machine learning, to improve business operations, drive innovation and improve the customer experience. Read here

One of the main pain points in running an AI business is counteracting misunderstandings people may have around AI and its capabilities, says Mark Nicholson, CEO, Vivacity Labs which improves traffic lights with artificial intelligence.

There is an inherent distrust in the technology and overcoming the negative perceptions that some people have when it comes to AI, especially among a more traditional demographic, is a significant challenge according to Philip Marshman, founder and CEO at Sentai a startup using advanced AI to support informal caregivers look after the elderly.

To overcome this, Nicholson and Marshman have put data privacy and trust at the heart of their companys product development and that should be the first thing entrepreneurs consider when setting up a tech/AI company, according to Nicholson.

In 2019, London-based investment firm MMC Ventures, in association with Barclays, analysed 2,830 AI start-ups in Europe and based their findings on public information and interviews with executives. It found that 40% of these companies did not actually use AI ahuge problem for startups is the amount of AI hype in the market.

Cha from Rahko explains that tempering expectations through hype is always going to be tricky.

While fundamental advances in AI are always exciting and important, a lot of the critical work in graduating from hype is in making things work in the real world, which comes with its own set of challenges. This is doubly true for quantum computing. Quantum computing captures peoples imaginations which makes for a field that is very prone to wild hype. We are extremely excited about the powerful new capabilities that quantum computing will bring, but like with AI, we do not focus on technological advances that do not strongly seem like they will be able to solve a real, currently intractable problem.

Hammad from Arctic Shores agrees and says that its all too easy to create a product thats great in theory but doesnt have its users at heart. One challenge is to constantly keep the customers challenges in mind which problems are you solving?

AI is a fast growing industry and competition is fierce.

Daniel Cooper, managing director at Lolly Co the digital transformation partner, sees the increasing amounts of competition on the web a significant pain point for AI startups.

SEO has becomehyper-competitive, even when factoring in individual niches. Getting noticed on the internet isbecoming increasingly difficult, and is driving a return to more traditional PR methods to grabGoogles attention. Simply put, there is always someone who is getting higher traffic than you, he says.

Every industry, even AI has been disrupted by Covid-19.

From a technology perspective, AI businesses have had to learn and adapt quickly to ensure their technology can be implemented with minimal disruption, and while the pandemic was unexpected, adapting to these challenges has allowed many tech businesses to thrive, says Tim Weil, CEO, Navenio the indoor location solution.

Tom Reiss, CEO at Roby AI the autonomous machine that empowers property managers, also says the pandemic has created a number of challenges for his company because of the impact on the sector that it serves real estate. Like many businesses, weve had to adapt quickly to our customers needs and the changing pace of the market, he says.

Both Ben-Porat from Edgify and Hammad from Arctic Shores agree that focus is a challenge for any AI startup.

Ben-Porat explains: Since AI seems like magic to most low-tech industries (for example, retail, manufacturing, automotive, etcetera) it is difficult for them to focus on one key pain point that they are trying to solve with AI. In most cases, they also just want to say that they are using AI across the board, rather than focusing on one use case where they will be using AI to solve a specific problem.

Hammad agrees but from a different angle. He argues its far too easy to get sidetracked from building and improving your core product, either by darting from one product avenue to the next, or by trying to fit your product to a particular client. Do one thing, and do it well.

AI has the potential to produce radical change in any organisation, if used correctly.

Pointing to the HR industry,Dr Alan Bourne, CEO and founder at Sova the AI recruitment service, explains that businesses can make huge long-term gains by automating elements of the recruitment process, like CV scanning and interview bookings. The biggest impact AI can bring to the HR industry is overhauling the fairness and accuracy of how people are selected for a job, by eliminating elements of inherent bias that hamper traditional, human led recruitment processes.

However, he suggests that making radical changes using AI carries an inevitable risk versus payoff quandary.

The stakes of challenging the status quo are huge, making the expectations much higher for the new solution to significantly outperform the original offering. Guiding people through the change process here needs to be based on genuine and thorough rigour, in order to offset decision makers risk levels.

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