Data Analytics

Big data analytics emerged as a buzzword back in the early 2010s, peaking sometime around 2015 when Gartner dropped it from its hype cycle and reported on its demise. While big data isn’t the buzzword it was a decade ago, it has bounced back from its “demise” in a big way, proving it’s much more than a passing trend. In the 2010s, big data failed to fully-catch on because there was a gap in the amount of data being generated, and the infrastructure and technology required to support it.

Today, the landscape has changed dramatically.

Advances in Artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) have converged with big data, ushering in a new era in data analytics—where real-time insights and embedded AI are accessible to a broader audience.

While the transformative potential is now a reality for leading organizations, many businesses still struggle to become truly “data-driven,” a problem that will only worsen as big data gets much bigger. In this guide, we’ll explore the current data analytics landscape, emerging trends, and where we think big data is heading.

What is Data Analytics?

What is Data Analytics

Data analytics describes the science of analyzing raw data to identify patterns, trends, and answers to questions—and is generally used to deliver insights used to inform a business’ strategy.

Data analysis isn’t exactly new. We’ve long relied on historical data to inform the best path forward. Only up until recently, data analysis was a manual process performed by humans.
Today, data analytics has evolved. We now use platforms with built-in AI that captures, analyzes, captures, categorizes, and delivers actionable insights to human users.

In this article, we introduce the concept of big data and provide a basic overview of how it works, then examine the role of big data analytics in the modern business landscape. Read more.

Evolution of the Big Data Analytics Market

Evolution of Big Data Analytics

Big data has come a long way in such a short time, thanks to a few key drivers pushing the market into the future.

We’re seeing rising IoT adoption, affordable out-of-the-box platforms, and widespread use of embedded AI and machine learning capabilities.

In this article, we look at the origins of big data analytics and explain how we went from pre-internet database management systems to today’s massive, high-speed, high-variety data landscape. Read more.

Current State of Analytics and Business Intelligence

Current State of Data Analytics

According to IDC, data is the “lifeblood” of digital transformation. As of 2019, 80% of companies say they leverage data across multiple internal processes, including market intelligence, product management, finance, logistics, and manufacturing.

That said, IDC researchers also found that data workers spent too much time searching for and preparing data. Microstrategy’s 2020 Global State of Enterprise Analytics found that many companies aren’t as “data-driven” as they think despite the big push toward self-service analytics.

This article examines where big data analytics and business intelligence solutions are today and how far we still have to go before most companies can say they’re truly data-driven. We examine the current trends shaping the big data and analytics landscape, including augmented analytics, which use AI and machine learning to help businesses make better decisions. We also discuss the current data science skills shortage, and how self-service solutions are helping companies close the gap, then we touch on the importance of creating a data-driven culture. Read more.

Big Data is Transforming Industries in Big Ways

Data Analytics Use Cases

Big data analytics has changed the game for businesses across every sector, from healthcare and financial services to retail, manufacturing, and even sports.

This transformation is a result of several converging factors, including growing IoT adoption, the rise of cloud-native platforms, and an explosion in internet use thanks to streaming services, social media, and of course, the smartphone.

In this article, we discuss how today’s world really runs on big data and take a look at a diverse (albeit far from comprehensive) list of use cases that prove big data and analytics are requirements for doing business today’s complex environment. Read more.

Real-Time Data Processing for IoT Applications

Real-Time Data Processing

The internet of things (IoT) is gaining traction across a diverse range of industries, including manufacturing, logistics, and retail. IoT-connected sensors and devices capture valuable data at high volumes and high speeds from all sorts of “things,” including manufacturing equipment, drones, fleets, and security cameras.

Big data analytics platforms, AI, and machine learning are converging with IoT systems, allowing organizations to extract and act on insights from these massive datasets in real-time.

This article looks at how real-time analytics help businesses capture the business value of their IoT applications. Read more.

Selecting the Right Business Data Analytics Tools & Platforms

Data Analytics Tools

Whether you’re in finance, healthcare, or logistics, big data analytics are becoming a critical success factor that unlocks strategic decision-making, reveals new revenue streams, and mitigates risks.

Still, data isn’t all that useful on its own, and without the right tools in place, you risk losing ground to your competitors.

This article focuses on the evaluation criteria for selecting the right data analytics solutions based on your core business objectives. Read more.

Why Data Analytics is Too Important to Ignore

Importance of Data Analytics

Experts have long warned that big data was too big to ignore (Deloitte called it back in 2013.) After a while, the chatter around data analytics started to die down as the technology available to most organizations hadn’t entirely caught up to the promise of big data.

Today, the predictions about big data are beginning to take shape. Big data converges with innovations in other transformative areas like AI, machine learning, the internet of things (IoT), and self-service reporting tools that bring data science to the masses (or rather, a reasonably tech-savvy business user).

In this article, we examine some compelling reasons why it’s time to take big data analytics seriously this time around. We also examine what needs to happen to make data transformation not only a reality but a competitive advantage. Read more.

The Business Benefits of Big Data Analytics

Benefits of Data Analytics

By now, most companies understand that investing in big data analytics is a step in the right direction. A recent MicroStrategy report revealed that 90% of participants say that data analytics plays a vital role in their company’s digital transformation efforts. As a quick aside, it’s worth noting that “digital transformation” isn’t really possible without data analytics.

Across the board, data analytics stands to have a positive impact on the bottom line, promising cost savings, better decision-making, and the ability to really get to know your customer.

This article focuses on the business benefits of data analytics, spanning a range of use cases. Read more.

Current Issues and Challenges in Big Data Analytics

Challenges in Data Analytics

IDC research found that only about 35% of organizations have a fully-deployed analytics system in place, making it hard for businesses to truly operationalize big data.

Researchers at Dun & Bradstreet reported that the biggest challenges for companies were protecting data privacy, ensuring that data was accurate, and developing a system for processing and analyzing data.

According to the Journal of Big Data, researchers found that numerous big data issues could be blamed on its inherent uncertainty.

The past few articles have focused mostly on the transformative potential of big data analytics. While we’ve touched on some of the challenges that come with the territory, we’ve yet to dig into the issues organizations face as they try to put big data to work. In this article, we look at some of the biggest obstacles companies must overcome on the road to becoming “data-driven.” Read more.

What is Augmented Analytics?

Augmented Analytics

Augmented analytics is a term coined by Garter that describes the use of artificial intelligence, machine learning, and natural language processing to enhance the value of big data analytics.

Analytics platforms have been around for a while by way of countless cloud-based applications. However, most solutions aren’t that great at surfacing the “why” behind the insights presented in their reports. The other issue is, gathering high-level business intelligence that goes deeper than say, your basic website analytics, is often expensive, slow, and requires the assistance of a data scientist.

Augmented analytics automates data management, delivers actionable insights, and is increasingly making it possible for the average business user. Think sales, marketing, or C-suite decision-makers–to generate reports and visualizations by entering a simple query. In this article, we explain what augmented analytics are and how they’re changing the way companies handle business intelligence. Read more.

A Roadmap for Implementing Big Data Analytics

Implementing Data Analytics

Big data is no longer the exclusive domain of tech giants and well-funded startups. Analytics platforms have become more affordable and accessible, creating new opportunities for mid-size firms and less tech-forward verticals to turn insights into tangible value.

These days, manufacturers, logistics companies, and the financial services sector have embraced digital transformation and are ramping up investments in big data analytics. That said, just because these solutions have become more widely available, companies with little experience with data, or analytics in general, face some major challenges.

In this article, we walk you through the steps of building a framework that can support your data strategy—even as it scales. Read more.

Best Practices for Managing Big Data Analytics Initiatives

Data Analytics Best Practices

A 2018 Forrester report revealed that companies considered “insight-driven” are on track to hit $1.8 trillion in annual revenue by 2021, growing by an average of 30% annually.
As mentioned up top, many organizations “feel” like they’re “data-driven,” yet few can maximize the full potential of their data.

This article lays out some best practices for managing your business’ data analytics strategy. Within, we cover the importance of defining your objectives, breaking down silos, and establishing a culture where data is “everyone’s job.” Read more.

Data Analytics Cybersecurity Best Practices

Data Analytics Cybersecurity

As data analytics investment and IoT adoption ramp up, new opportunities for companies are opening the door to new opportunities for bad actors to exploit a whole new system. Unfortunately, security remains a significant challenge for companies as they build out their big data strategy.

A 2019 CrowdStrike report revealed that threat detection was a significant issue for companies. Researchers found that a whopping 95% of businesses fail to meet the standards of the 1:10:60 rule and that, on average, it takes 162 hours to detect and contain a data breach.

According to a 2019 IDG Cybersecurity Priorities Study, compliance, internal threats, and learning to use data responsibly are among organizations’ top struggles as they adopt new solutions. Another research paper, the Fourth Industrial Revolution, revealed that 45% of data leaders say data visualization is the biggest obstacle to reaching security objectives.

Challenges aside, new solutions are emerging to help companies tackle their biggest security issues, including real-time AI-driven analytics platforms, data mapping, and machine learning. In this article, you will discover how big data analytics can be used to get ahead of the cybersecurity threats caused by big data. Read more.

Improving Customer Experience with Data Analytics

Data Analytics and the Customer Experience

Going into 2020, Forrester predicted that this year, companies would either learn to use customer insights to drive business value or find themselves vulnerable to savvier competitors. Adobe’s 2020 Digital Trends report revealed that companies that were considered CX leaders were three times as likely to have exceeded last year’s business goals.

Sure, focusing on 2020 predictions seems pointless amid a pandemic. However, both reports (among countless others) were right about CX. Customer experience is becoming a major advantage in every vertical from logistics to healthcare, and big data now plays a critical role in helping companies serve up the personalized, relevant experiences they crave–at scale.

This article explains how AI, machine learning, and advanced business data analytics platforms are helping companies of all shapes and sizes embrace their humanity and gain a competitive edge. Read more.

A Data Analytics Strategy for Mid-Sized Enterprises

Data Analytics Strategy

According to a recent IDC report, researchers found that mid-size companies that prioritize digital transformation are twice as likely to see a double-digit increase in revenue and four times less likely to report losses than similar enterprises that fail to embrace innovation.

What’s more, as data analytics, AI, machine learning, and predictive modeling are becoming accessible to a broader range of businesses, mid-size enterprises are now able to compete with big players once considered out of their league.

And while large corporations are still likely to have the upper hand when it comes to resources and talent, mid-size companies often have an advantage when it comes to agility. Those mid-size players that nail intelligence now stand to emerge as the big winners in the next era of big data analytics. That said, many mid-size organizations still struggle to realize the full potential of their big data strategies, whether due to culture issues or scalability limitations.

In this article, we outline some of the technologies mid-size companies are using right now, including predictive analytics, self-service BI, and customer intelligence, and discuss why IoT is a growth driver in this space. Read more.

KPIs to Measure ROI from Big Data and Analytics Initiatives

Data Analytics KPIs

Calculating your return on investment (ROI) for big data projects isn’t always easy. For one, big data projects cover a diverse set of use cases, and two, many companies have multiple projects happening simultaneously. Each of those projects could be targeting a different set of problems and be in varying stages of maturity.

As you might imagine, measuring the ROI of big data and analytics isn’t some monolithic activity that can be contained in a single report and measured by a universal set of KPIs.

In this article, we dig into the importance of measuring the success of your data analytics initiative one use case at a time. We also take a look at a few things to consider when determining the KPIs you’ll use to track progress. Read more.

Putting AI to Work to Derive Insights from Data Analytics

Data Analytics and AI

Counter to the idea that AI would eventually take our jobs, in many ways, it’s become a supportive “collaborator” that allows us humans to create more value.

AI analytics are transforming the entire business landscape in a variety of ways, including improved data literacy, guided selling, personalization, anomaly detection, and countless other use cases.

In this article, we examine how AI is changing the game for human workers and at the machine-to-machine (M2M) level. Read more.

Data Analytics Drives Business Intelligence

Data Analytics and Business Intelligence

Traditional business intelligence (BI) can’t keep up with the demands of big data. It typically requires an expert to build models, generate reports, and analyze findings—a process that can take hours, days, or even months.

As big data converges with AI, machine learning, and automation, business intelligence can break away from the data science department, empowering business users across the org chart to run reports and make decisions.

In this article, we cover how data analytics and business intelligence join forces with other transformative technologies to deliver valuable insights for companies operating in every sector. We also explain how business intelligence is evolving, with emerging trends like mobile BI, natural language processing (NLP), and data visualization democratizing the space. Read more.

Creating Business Value with Data Mining and Predictive Analytics

Creating Business Value with Predictive Analytics

According to a Splunk report, The State of Dark Data, over 70% of respondents say they expect data to become more valuable within the next decade. Over 75% say that companies that can capture the most data stand to gain a competitive advantage. Yet researchers estimate that over half of all organizations’ data is considered dark. For the uninitiated, dark data is a term that describes data that is unknown, unused, or inaccessible.

Assuming most companies are generating more and more dark data, “more is better” mentality doesn’t really work without the right infrastructure and big data strategy in place. To get ahead of ever-expanding datasets–dark or not–organizations are increasingly leaning on two critical processes: data mining and predictive analytics. Data mining uses software to gather, clean, and transform data, then surface insights from unrelated data sets.

While data mining provides a picture of what’s happening, it doesn’t tell you what to do about it.
That’s where predictive analytics comes in. Predictive analytics tools use AI, machine learning, NLP, and other technologies to analyze data to predict future outcomes.

In this article, you will learn the role both data mining and predictive analytics play when it comes to creating business value–and the close connection the two processes share. Read more.

Using Analytical Decision Making to Improve Business Outcomes

Improving Business Outcomes with Data Analytics

A recent AdWeek article reported that almost half of 2020 business insights are no longer relevant. In the wake of the current pandemic, businesses are beginning to realize that their forecasting capabilities aren’t as strong as they thought. As a result, organizations are looking toward solutions that offer accurate, real-time insights and predictive modeling so that they’re ready to pivot on-the-fly when the next black swan event changes everything.

Another recent article this time by Boston Consulting Group reported that advanced analytics platforms are essential for helping companies thrive in uncertain times as they address three key areas: detection, multivariate modeling, and contingency planning.

Even in “normal times,” businesses with a strong analytics culture stand to fare better in today’s competitive landscape. A Deloitte survey found that while nearly 70% of executives say they aren’t comfortable using data, 37% of survey participants with the strongest analytics strategies were twice as likely to exceed their business objectives than participants with the weakest cultures.

In this article, we examine the importance of developing a data-driven culture and look at some of the factors that must be in place for that to happen. We discuss how to lead with big data and analytics, how to equip your workforce with the training and tools most relevant to their job, and how to pick the platforms that best support your team. Read more.

Ensuring Success by Partnering with a Mature Data Analytics Company

Data Analytics Partner

As big data becomes a requirement for doing business, companies need to not only learn how to rein in their ever-expanding datasets but also turn that raw data into real value. Unfortunately, the path to becoming a “data-driven” business is hard to navigate unless you know what you’re doing.

Data analytics companies help organizations identify the best use cases and select the right platforms for achieving critical objectives. They’ll also help you set up the right infrastructure, select the most valuable data sources, and develop an integration plan.

In this article, we outline the many reasons why partnering with a data analytics company is the right move for ensuring that your big data initiative is a success. Read more.

The Future of Data Analytics

Future of Data Analytics

While it’s hard to predict what’s next for any industry in a global pandemic, we see signs that big data analytics is on the verge of another significant disruption that will change the way we do business.

We’re currently at an inflection point where several “transformative” technologies like AI, the IoT, machine learning, and the cloud are converging with big data to unlock new opportunities. Think real-time data streaming, self-service BI, and the ability to capture data from legacy machinery.

In this final article, we look at emerging trends that stand to shape the future of data analytics in the years to come, including augmented analytics, continuous intelligence, X analytics, and more. Read more.

Tiempo’s Data Science consulting and development solutions can help your company master big data analytics from platform selection and strategy to training and implementation. Contact us today to learn more.