Analytics Pitfalls—A Check List
Gartner predicts that through 2017 60% of big data projects will fail to go beyond piloting and experimentation and ultimately will be abandoned. In this article, we’ll outline the potential pitfalls that account for this low success rate and suggest ways you can address them.
There are several internal challenges that impede success: data-related, team-related, and even at the executive level.
Leadership Team Challenges
Consider the following external challenges to why analytics projects fail.
Common Misconceptions About Predictive Analytics
Analytics is a one-time effort.
False. You can’t just set everything up and reap the benefits. Done right, analytics involves a continuous feedback loop – it never stops.
It’s a black box effort.
False. The project must happen transparently, in the open, with immediate disciplined, regular feedback.
Actionable insights require big data.
Actionable insights don’t require big data. Some of the most valuable business insights are derived from surprisingly small data sets (it also costs less and minimizes risk to start small). Instead of focusing on big data, we recommend focusing on the right data at the right time and finding a way to ask the right questions of that data.
There must be a tool for this.
Unfortunately, not. There’s no such thing as a predictive analytics tool that you can install, press a button and marvel at your insights. Instead, many enterprises invest in more than one tool (some on-premise, some in the cloud), each of which requires customization. And not all these tools will be future-ready and may need to be switched out with time. As your business needs change and digital transformation reaches a new step on the maturity ladder, your choice of tools will also change.
My data will give me 100% certainty.
False. There is no 100% certainty. In many cases, a margin of error of 30-40% accuracy may be good enough.
Actionable insights are essential.
Not always. Sometimes the goal could be to achieve no actionable data from your efforts. For example, if you’re using data and predictive analytics to alert you of potential equipment failure in a medical environment, no data in the form of alerts means there’s nothing to report and all is well with the equipment.
New, Nimbler Approaches to Analytics
Though there isn’t an approach that avoids all above-mentioned pitfalls, there are methods that help to avoid at least some of them. We’ve introduced several best practices into our client engagements that break down these barriers and produce rapid, iterative, actionable insights and give management what they need without alienating data owners or breaking the bank.
1. A Software Development Approach
This is a novel way of looking at things, but data analytics projects have many parallels with software product development. Instead of delivering a piece of software, you’re delivering a data product and product/software development best practices still apply. Yet, most data analytics consultants and external vendors don’t have that product mentality. They prefer to focus on maximizing billable hours. This prevents the transfer of knowledge and intellectual property to the product owner who is going to run the tool, aka the customer.
Just as a proof of concept (POC) in software development must prove the unknown, the POC in an analytics project uncovers any impediments to delivering value in the fastest possible way. Once the POC is affirmed, you are led to your first minimal viable prediction (MVP).
An MVP approach is all about disregarding the noise and assembling only the data that correlates with your number one problem, as fast as you can, and iterating from there. While your executives or business sponsor doesn’t need to be involved all the time, be sure to schedule periodic, short feedback loops. Make it your goal to deliver minuscule pieces of fast progress. This will ensure they see immediate, incremental results, get exactly what they need, and drive greater engagement.
The MVP approach goes against the grain of an all-in, high-risk, long-haul data analytics practice. It also keeps you focused and helps you avoid many of the pitfalls above. With this kind of discipline, you’ll find it easier to say no to frivolous feature requests, charts, graphs, etc. because you need something actionable ASAP.
Adopting an iterative approach, rather than diving into your big data all at once, can pay dividends and put AI-driven decision-making quickly within your reach. This sounds straightforward, but it can be tempting to jump into the data lake in front of you instead of just following small, relevant, iteratively assembled data breadcrumbs.
Read more about how to implement MVP.
2. API-in-a-Box Approach
Unfortunately, a software development approach to analytics doesn’t solve the problem of disparate systems. You may have the right method, but if your systems don’t talk to each other, getting to the data is challenging.
Data ownership, or fear and fiefdom, often impedes or makes sharing data difficult. While you can counteract fiefdom through evangelism (it behooves your organization to prove how sharing data creates value across the board), we’ve come up with a concept to aid in this effort. We call it “API-in-a-Box.” API-in-a-Box has another benefit, it allows systems to quickly talk to each other without having to conduct a costly and time-consuming traditional system integration.
API-in-a-Box breaks out data silos without data owners fearing they’ll lose control of their data. By packing all relevant technology into a container and giving each department access to the data that’s relevant to them (or that the data owner feels comfortable sharing) via an API – silos are easily overcome. An API-in-a-Box can be spun up in days, eliminating the time-consuming data integration problem. Plus, after data errors are found and one department’s data is merged with another, actionable insights start to emerge and the barriers of fear and fiefdom start to break down.
3. Internal vs. External Data Sources Concept
We also recommend moving away from internal vs. external data sources concepts. For your analytics project, it shouldn’t really matter where your data comes from. Use as many relevant data sources as you need and as few as you can. Try to create and maintain as little data as is necessary. If someone is already collecting, cleaning, and making that data available, why should you replicate their efforts? Data can be readily leased or purchased. For example, weather data is available for purchase and is much cheaper than collecting your own. Some companies generate so much data that it even generates new business opportunities.
Externally collected data can even have a monetization value. A client assembles sports data from national leagues, packages it into an API, and licenses it. All the data is created and maintained by the leagues, but the company simply utilizes that data and provides it to others who may not have the resources to assemble that data for a fee.
New Technologies Enabling these New Approaches
Getting started on any data analytics project can take several months – all that data gathering, cleaning, structuring, model building, enhancing, and reviewing takes time. However, emerging technology is enabling nimbler approaches. Cloud services, for example, have revolutionized analytics. Everything you need to crunch data is available out-of-the-box. Prior to these developments, you’d have to work with Software-as-a-Service (SaaS), manage your own data center, and deal with non-user-friendly processes. Quite frankly, it was a pain. Here are just a few examples of the numerous cloud services for analytics:
Cloud services are democratizing analytics and putting actionable insights into the hands of IT and non-IT teams in ways that weren’t previously possible.
Of course, data analytics is more than just best practices and data integration/storage/crunching tools. Data scientists often need access to huge computational power quickly, on very short notice, and for a short period of time. Likewise, proximity and access to cognitive services like artificial intelligence (AI) and machine learning in the cloud (Microsoft Azure and Amazon Web Services) are needed in unplannable ways.
Technologies such as Kublr enable data scientists to move huge amounts of data between clouds, the data center, or wherever your data needs to go. This provides unprecedented access to data and computes power that can scale up and down as quickly as you need it.
What’s Next? Food for Thought as Your Data Analytics Projects Evolve
Hopefully, these best practices and technology insights have provided some food for thought about how you approach your next data analytics project.
But don’t stop with your first MVP. As your organization’s analytics capabilities mature, you’ll be able to incrementally feed your engine more data—in terms of volume and diversity. First, you start understanding simple correlations, then you’ll start getting information, then you’ll get predictions and in the next step, you receive recommendations. As your system matures, it will slowly turn into an AI engine.
One final area to ponder are advancements in AI and how that factors into your BI strategy.
More and more decision-making is being delegated to machines. Whereas in the past machines were relied upon for alert-only notifications, now they enable real-time, context-aware decisions. For example, AI monitors and auto-corrects manufacturing processes, help the C-suite make strategic decisions, and marketers determine which promotions they should offer.
AI can seem overwhelming. But just as you approach your next data analytics project by starting small, iterating constantly, and providing regular feedback to business sponsors, apply the same baby step approach to AI.
While traditional computers continue to evolve and pump out more raw power, they are no match for the quantum computer, which can tackle calculations that the most powerful conventional machines would need decades to process – in a split second.Read more
HackTJ 2002 is in the books, bringing together more than 400 bright young minds eager to tackle real-world problems with creative technology solutions. As a Gold Sponsor for the event, EastBanc Technologies created three challenges for the young innovators, and we are delighted to announce this year's top contestants -- and their winning hacks.Read more
This is EastBanc Technologies 3rd year sponsoring HackTJ, and our participation includes designing three challenges for teams to hack. The challenges will explore how to alleviate some of the world’s most pressing issues impacting our personal and professional lives.Read more
One “new” technology that has stuck is Blockchain. To understand what Blockchain is, you only need to know three things. What is a block? What is a chain? What is a ledger?Read more
Modern technology brings the world closer together, but millions of people continue to be left behind. The "digital divide" is multifaceted and impacts society in a variety of ways. These are some of the technologies that are helping bridge the gap.Read more
Artificial intelligence (AI) can be found almost everywhere in modern life. Learn key lessons and best practices that help companies avoid common AI pitfalls and achieve ROI from their AI systems.Read more
Open Data fuels today's digital economy, enables communication and innovation, boosts business and generally makes our lives easier. But how do we protect privacy if everything is open? Zero-knowledge proof could be the answer.Read more
Blockchain capabilities, including fully-automated data storage and transparency, make it an essential technology for cybersecurity. In this article, we look at some of its use cases.Read more
DevOps built-in flexibility allows development teams to work at a level that suits their resources and skills without being held back by departmental barriers.Read more
Artificial Intelligence (AI) – the capability of a machine or piece of software to display human-like intelligence – permeates our daily lives, often in ways we do not notice.Read more
Data-driven software touches our lives every day. Sometimes, it is in ways you see, such as when you check your Twitter feed, pay for your bus ticket or order your latte using your phone.Read more
EastBanc Technologies is recognized on CIOReview’s list: “Most Promising Microsoft Azure Solution Providers.”Read more
In this article, we’re going to dig a bit deeper into AI-implementation. We will take our airline use case a step further, and we will describe a specific example of how EastBanc Technologies solved a particularly challenging problem through AI and machine learning.Read more
If your organization provides a product or service -- which applies to just about any business on the planet -- you, too, can benefit from Artificial Intelligence (AI). While implementing AI may sound daunting, it doesn't have to be complex or expensive. This article covers the basics of AI and looks at some easy-to-explore use cases.Read more
Digital transformation is about opportunity and survival. Businesses that transform digitally gain a significant competitive advantage.Read more
Part 2: Best practices for modernizing your company’s IT infrastructure to ensure innovation success.Read more
Best practices for modernizing your company’s IT infrastructure to ensure innovation success.Read more
Learn how machine learning engineers and data scientists collaborate and roll out models faster and with ease using Azure Machine Learning.Read more
What is DevOps, what are DevOps practices, and how do you implement DevOps? Your FAQs answered.Read more
Refactor, rewrite, or leave as is? Learn when and how to bring your legacy systems up to speed with modern application development practices.Read more
Learn how technology can better meet your business needs with this foundational understanding of how software and system architectures work.Read more
Ready to embrace AI? Explore why cloud computing is the best infrastructure for your AI model, not on-premises.Read more
Software is a strategic differentiator that can catalyze digital transformation. Organizations are investing in technology, such as modern cloud services, to drive efficiencies and increase the customer experience. To make this a reality, it’s essential that business leaders have a basic understanding of business software and applications work and the opportunities they bring.Read more
How an intelligence-driven customer technical support approach can transform your support from a reactive operation to a streamlined, efficient, and proactive operation.Read more
Kubernetes is a popular container orchestration system, but how did it come to be and why, and what role does it play in digital transformation?Read more
Continuous integration and continuous delivery (CI/CD) is integral to a DevOps approach to software development. But what is CI/CD and why is it key?Read more
This article is the third in a series that aims to demystify data science , machine learning, deep learning, and artificial intelligence (AI) – while exploring how they are interconnected.Read more
2020 has seen profound change in the way we live and work with COVID-19 accelerating the pace of digital transformation. Yet, business leaders are often confused about how to implement one of the key enablers of...Read more
Artificial intelligence (AI), together with its brethren buzzwords data science, machine learning, and deep learning have been around for some time now and are no longer future concepts. Yet misconceptions persist about the true meaning of these terms.Read more
When SUSE, the world’s largest independent open source company, announced its acquisition of Rancher Labs in early July 2020, the industry took notice. Clearly, the Kubernetes management industry is very much alive.Read more
We live in a technology-driven world. Even non-technology companies are seeing their business models increasingly shaped by technology. Led by disrupters such as Amazon and Netflix, those enterprises who recognized opportunities early have found ways to extend the analog experience into a digital one. Even creating new revenue streams that they could never have predicted.Read more
Digital transformation is about delivering core competencies in a digital, automated, and user-centric manner. Driven by data and powered by tech (e.g. cloud, cloud native stack, AI, machine learning, and deep learning), it increases business agility, competitiveness, and enhances customer value.Read more
Let’s start by understanding where DataOps falls in the line-up of current IT methodologies. DataOps is the next level up from ETL (extract, transform, and load) and MDM (master data management systems) in terms of organizing data and processes. It can also be thought of as a methodology that combines DevOps and Agile within the field of data science.Read more
The hotel industry hasn’t changed much in the past decades. While they have introduced some level of digitization such as websites and apps, they haven’t fully embraced digital transformation. Indeed, if things are working fine, why change? Because the next unforeseen disruptor may be right around the corner.Read more
The term “DataOps” has picked up momentum and is quickly becoming the new buzz word. But we want it to be more than just a buzz word for your company, after reading this article you will have the knowledge to leverage the best of DataOps for your organization.Read more
Unstructured text is found in many, if not all business functions, and can become a source of valuable insight. Product reviews will guide your customers’ preferences, customer support chats can identifyRead more
Disclaimer: We have not spoken to a WeWork executive and have no further background information. This is merely a thought experiment to exemplify what digital transformation is about.Read more
In part one of this series, we defined data science and explored the role of a data scientist — including data preparation, modeling, visualization, and discovery. We also introduced the role of a machine learning engineer who closely collaborates with the data scientist.Read more
Big data continues to grow exponentially creating a critical need for solutions that can make sense and extract valuable information from it. For example, the Internet is full of a wide variety of constantly growing text sources— blog posts, forum posts, chats, message boards, item and services reviews, etc.Read more
Kubernetes, the de facto container orchestrator, is great and should be part of any DevOps toolkit. But, just as any other open source technology, it’s not a full-fletched ready-to-use platform.Read more
With the increasing popularity of machine learning (ML), it’s becoming more difficult for data scientists to find the appropriate tools for a specific task and decide on a robust approach. Should they stick to the basics and code everything from scratch or use one of the many pre-built tools that keep popping up on the market?Read more
Blue-green deployments and canary releases mitigate application deployment risk by enabling IT to revert back to the previous version should an issue occur during the release. Switching back and forth between versionsRead more
For those who were still debating whether they should hop on the digital transformation bandwagon, the COVID-19 crisis was a wakeup call, maybe even a slap in the face.Read more
The entire business world is talking about digital transformation. IT leaders, on the other hand, talk about DevOps, cloud native, Kuberentes and containers.Read more
If your organization leverages technology as a differentiator, a DevOps approach to application and service delivery is inevitable. The benefits are just too great.Read more
Digital transformation is one of today’s biggest buzzwords. Everyone is talking about it; everyone wants it. We all know the role technology is playing in enabling businesses to innovate at an unprecedented pace.Read more
The data on big data indicates that up to 60% of analytics projects fail or are abandoned, costing companies an average of $12.5 million. That’s not the result we seek from data lakes. Instead, companies are increasingly finding themselves mired in data swamps that are overfilled and too muddy to offer any useful visibility. Or are they?Read more
We collect data at a mind-boggling pace. In fact, as companies, we’re hoarding it. But what good is data if it can’t speak to us? Fortunately, data complexity can be broken down through design and visualization – the charts, graphs and plots that show trends, outliers and opportunities.Read more
As a company and as a team, our lives at EastBanc Technologies have always been about tackling the biggest problems for the biggest organizations.Read more
Artificial intelligence (AI) surrounds us. It unlocks our phones, creates our shopping list, navigates our commute, and cleans spam from our email. It’s making customers’ lives easier and more convenient.Read more
Nearly every week there’s something new in our industry. The pace of technology is unprecedented, the role of IT is booming, and innovation is part of our DNA.Read more
Technology is accelerating at such a rate that it permeates all industries. In fact, software is the only industry that cuts horizontally across all verticals.Read more
Innovation is a critical part of business. While prioritizing production in general makes sense, the best approaches make innovation a component of the whole production process.Read more
We recently sat down with a large pharmaceutical company to discuss their data analytics projects. What we heard wasn’t a surprise. Three of the four large analytics efforts they undertook last year had failed.Read more
AMS Group is a cohesive group of established companies that provide technology and security equipment to aerospace, defense, and security markets.Read more
A European market leader in online survey and feedback software acquired complementary companies in different Wester European countries, each of which had its own survey platform.Read more
Everyone loves their own data. Collecting it. Analyzing it. Drawing conclusions from it. But often, when you allow departments or business units within your organization to gather their own data, that data isn’t shared.Read more
Organizations generally understand the power behind analytics, but how do you make it work culturally and technically? We take a look at the barriers to data analytics success and suggest new approaches that buck the system, with dramatic results.Read more
And how to make your next data analytics project succeed?Read more
Container use is exploding right now. Developers love them and enterprises are embracing them at an unprecedented rate.Read more
If you’re making the move to containers, you’ll need a container management platform. And, if you’re reading this article, chances are you’re considering the benefits of Kubernetes.Read more
Wouldn’t it be nice to reach artificial intelligence (AI) nirvana? To have a system that provides real-time, context-aware decisions.Read more
Today’s IT environment is moving and evolving at an unprecedented pace. So, all of a sudden, your 5-year old software infrastructure can look more like it’s 50. To get your software current – and stay there – requires flexibility. Moving to containers does just that. There’s been lots of talk about containers over the past few years – so why aren’t you on the bandwagon yet?Read more
Under pressure to deliver applications faster and ensure 24/7 runtime, organizations are increasingly turning to DevOps methodologies to deliver applications quicker and in an automated fashion. But what tools should you have in your DevOps toolkit?Read more
Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP) are the public cloud market leaders, but how do you determine which of them best supports your enterprise's specific needs? For most enterprises, and for the foreseeable future, it’s going to be a multiple answer question.Read more
As the dominant movie rental service in the 90s and early 2000s, Blockbuster was the market leader, seemingly indefatigable. Until the great disruptor, Netflix, hit the scene.Read more
Big Data. Everyone’s paying for it, collecting it, and talking about it, but what are companies actually doing with it?Read more
The API management market is a hot one. As more organizations make investments in mobile, IoT, and big data, APIs are a core of their digital strategy.Read more
Big data is everywhere. Organizations are being advised to hoard it and do everything they can to derive actionable insights. This article will argue that this approach puts the cart before the horse.Read more
Let’s face it. Organizations struggle with their legacy applications. Even when they still solve some of the business’ problems, they reach a point where they can no longer keep up with market and industry demands.Read more
Let’s flash back to 2000. You’ve survived Y2K and you’re building systems for CRM, inventory, logistics, or data. They’re all state-of-the-art, and get the job done, even if they don’t talk to each other.Read more
It’s a mobile app world, and we just live in it. But for those working on the “next big thing,” there’s a conundrum – everyone knows we should be building apps in HTML, but not every device out there runs it as smoothly as it should.Read more
In technology, everyone likes to talk about “future-proofing.” But even for the most cutting-edge tech, time always catches up.Read more
The future is here. No, we don’t have flying cars or robot butlers – yet – but it’s definitely a digital world.Read more
We’re excited to announce Microsoft Azure support for the Kubernetes auto scaling module, an open source system for automating deployment, scaling, and management of containerized applications.Read more
You can’t mention enterprise technologies today without getting into a discussion about the cloud. “Are you in the cloud yet?” Why jumping headlong into cloud computing may not be the necessary move for your business.Read more
In the mad rush to move to the cloud, some organizations put the proverbial cart in front of the horse. They’re just looking for the best hosting, the preferred provider, or whatever the rest of the industry is using.Read more
2016 saw momentum in many areas – DevOps, cloud technologies, and big data- at the thrust of innovation. So, what tech predictions will define 2017?Read more
Every month, week, or day, it seems there’s buzz about yet another solution or service that will revolutionize your industry – or more simply, make your life easier.Read more
Apps. Sensors. They’re everywhere. Your phone, your car, your TV, even your refrigeratorRead more
In an increasingly commoditized market, learn how to cut through the noise and forge a cloud strategy that meets your needsRead more
Fleet management is a challenging business. This is particularly true of snow removal services where the dynamics on the ground can change fast and the pressures to perform put fleet supervisors to the test – in the toughest of conditions.Read more
Long before the first flakes fall from the sky many municipalities begin to prepare for the cold, icy, and snowy conditions that inevitably lie ahead.Read more
Fun fact: in 2014, cloud services were already a $45 billion business worldwide, and are expected to grow to $95 billion by 2017. Will you be part of that equation?Read more
Simple is good. Simple is clean. And whether I’m cooking or planning a trip, simple is always better, right? So why do so many companies make user experience (UX) so complex?Read more
Future-ready predictive analysis infrastructures hold the key to gaining insights from data today, and into tomorrow.Read more
Immersive and exciting, Virtual Reality is already part of our lives, whether it’s a plot device in a new sci-fi thriller or the best way to enjoy the latest video games or thrill rides.Read more
Now that smartphones are the most widely used tool for navigating important life activities (nearly two thirds of Americans own one), there’s pretty much an app for everything these days.Read more
If you’re tasked with choosing an API management system, Charles Dickens summed it up best: “It was the best of times, it was the worst of times.”Read more
DevOps: the panacea for all that’s wrong with enterprise IT. Where siloed teams who keep information close to their chest are replaced by agile, transparent relationships between developers and operations and fast and stable workflows that improve IT efficiency significantly and very visibly.Read more
As a technology company focused on complex project integrations that unify legacy systems as well as modular solutions that ensure lasting scalability, we work on a multitude of projects that involve custom software development; packaged, open source, and SaaS software integration; infrastructure setup; and production operations and maintenance.Read more
In an earlier blog we talked about why you need to integrate API management into your business strategyRead more
In a previous release of “What the Tech?” we discussed why you should integrate API management into your business strategy.Read more
Smart cars, smart homes, smart devices. The Internet of Things (IoT) is already transforming how we live. But very soon, the IoT will swiftly extend into the enterprise.Read more
Why you Need to Integrate API Management into your Business StrategyRead more
The promise of big data is, well, big! With terabytes of intelligence at their disposal, organizations can make faster, more accurate decisions, monitor trends, and even predict the future.Read more
Businesses accumulate data, create content, or possess unique business logic—each of which represents an untapped business opportunity. But how can organizations realize that opportunity?Read more
The Internet of Things (IoT) is much more than a consumer trend, it’s rapidly changing the way enterprises are using data to improve business decision-making.Read more
Content consumption is changing rapidly. With multiple channels and media formats, reaching target audiences is getting harder than ever.Read more
The way in which we consume content is changing rapidly and a few trends have emerged recently that we think will have a meaningful impact on media organizations this year and in years to come.Read more
Building a mobile app isn’t as simple as it used to be. With multiple devices to cater to, development teams must ask themselves a few questions:Read more