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Mission to Leverage Decisions from DataGoodData was founded in 2007 with headquarters in San Francisco and Portland. The company’s mission is to fundamentally change the way businesses use data to make decisions. Many companies have not seen a return on their BI investment, and GoodData believes it is because the insights were not actionable, were not delivered in context or within the workflow. It has asked everyday business users to go out on their own and look at dashboards with no understanding of what to do with the information found on those dashboards. There was good intent behind self-service analytics, but in reality, it was not happening. People were not going to leave their daily workflow to look at a dashboard when that dashboard does not tell them what action to take. The company’s foundation has always been to embedding insights at the point of work and delivering at mass scale.
A Passionate Entrepreneur and Insightful LeaderAs the Founder and CEO of GoodData, Roman Stanek is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was the Founder and CEO of NetBeans, a leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).
Disruptive Contribution to the Big Data IndustryAs businesses have not received the ROI on their BI deployments as expected, GoodData is the leader in educating the public about embedded analytics. Many people don’t understand that embedded analytics is a different animal. GoodData is embedded into the business process and is contextual. The company is not selling technology to IT, instead is selling analytics applications to a business unit for a business problem. GoodData aims to solve domain analytics problems. By being embedded at the point of work, without having to leave the application in which clients are used to working, and because the company is contextual, clients know exactly what to do with the insights that are being presented to them.
Innovation and Partnerships Drive SuccessArtificial Intelligence and IoT are at the center of today’s innovation. All innovation includes one or both of these technologies and they will continue to grow in importance in the coming years. These technologies will define the competitiveness and success of a business.
GoodData promotes a culture of innovation. The company is continuously investing in the technologies that are disrupting the market and will separate successful businesses from those that are not. As mentioned, Artificial Intelligence and IoT are the cornerstones of today’s innovation and GoodData is fully invested.
Awards and Accolades for ExcellenceMost recently, GoodData has been recognized as a Leader in the Forrester Wave™, Enterprise BI Platforms with Majority Cloud Deployments, Q3 2023 as well as a Strong Performer in the new Forrester Wave™, Insights Platform-As-A-Service, Q3 2023. Good Data is used by over 50% of the Fortune 500 companies and there are over 1.2 million users on the GoodData platform. And, over 70,000 businesses use GoodData.
Challenges and Impediments to GrowthAccording to Roman, the biggest challenge in the company’s mind is that the industry has been focused on self-service dashboards while the company was focused on embedding insights at the point of work. GoodData was going against the popular trend because it knew that people would not leave their work environment to search for a static dashboard to look at visualizations that were difficult to interpret. It has taken some time for businesses and analysts to catch up to understanding that the only way to get ROI is to provide the insights with context and help guide everyday users in the actions they should take upon being presented with the data.
The Future AheadThe industry is recognizing the value of embedding analytics at the point of work within the user’s workflow, presenting considerable growth opportunities for GoodData in the years ahead. More companies will move away from self-service dashboards to embedding analytics at the point of work. They will also begin implementing machine learning for easily repeatable business processes, allowing frontline workers to spend more time on strategic business decisions.
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Automated Insights: The Pioneer In Delivering Natural Language Generation Technology To The Masses
Automated Insights was established in 2007 and headquartered in Durham, N.C. In 2023, the company was acquired by Vista Equity Partners, a $30B+ private equity firm, making it the first natural language generation company acquired by a private equity firm. The company is moving forward with a mission to make the world’s data understandable. Automated Insights was founded primarily with a sports industry focus, generating sporting event recaps and personalized fantasy football narratives, before soon realizing that there was a deep need for this type of technology across various industries. Automated Insights now serves clients and partners spanning over 50 industries in more than 20 different languages. Its goal is to empower organizations and individuals with the ability to make faster, more efficient business decisions, regardless of their level of expertise or position within the company.
A Passionate Leader with a Zeal to ExcelMarc Zionts, the CEO of Automated Insights, is a technology executive and entrepreneur who has been successfully leading and growing companies since 1987. He earned both his bachelor’s and master’s degree in management from the Georgia Institute of Technology and remains active at Georgia Tech as an Alumni Mentor. Zionts is passionate about working with emerging technology, introducing it to the market, and maximizing businesses commercially to drive value and further innovation. As CEO of Automated Insights, he is building upon the established foundation to unlock exponential growth and technology developments. Zionts is also an Independent Board Director for Pivot 3, TEOCO, a TA Associates portfolio company, and Friends of the Earth, a Washington D.C. based environmental group.
Significant Contribution to the Big Data Analytics IndustryAutomated Insights bridges the gap between the world of Big Data Analytics and Business Intelligence (BI) through NLG, which transforms large, structured datasets into human-sounding written analyses. Automated Insights’ Wordsmith authors data storytelling to reveal trends and provide role-based prescriptive reporting to make better decisions in real-time with otherwise challenging data. Using Ai’s platform, clients are given the ability to walk themselves through their data and are given what they need to know and what to do about it. Ai equips organizations with the most essential decision-making tool: an accurate, complete understanding of their data.
Leveraging the Power of Disruptive TechnologiesThe amount of data the world produces and absorbs is growing exponentially. Disruptive technologies like Big Data Analytics, Cloud Computing, IoT, and Artificial intelligence aren’t just impacting today’s innovation, but are the driving force behind innovation. Each of these technologies are interlinked, feeding into each other and pushing each other to new heights. There is a mass amount of information and data being created by the IoT which has revolutionized the way the world consumes data. Because of this rapid growth, cloud computing has been driven due to the sheer volume and speed of information by the IoT. Artificial intelligence is being applied to a multitude of technologies to quickly enable the processing of Big Data Analytics, IoT, and Cloud Computing which drives better decision-making on all fronts, regardless of the industry.
Industry Driven PartnershipsAutomated Insights’ Wordsmith offers unparalleled customizability to empower companies with the capability to take complete control of the output, weave a cohesive story based on the entirety of their data, and ensure insights are more accessible and easily shared within an organization. Being the first to offer a self-service platform brings immense value to its clients and partners and enables them to structure, edit, and generate data-driven narratives without reliance on an outside analyst to provide insights. Key involvements that have helped drive the company’s innovation are Automated Insights’ business intelligence partners, including Tableau, MicroStrategy, TIBCO, Qlik, Interworks and more.
Awards for BrillianceAutomated Insights is proud to be at the forefront of natural language generation, pioneering the world’s first self-service NLG platform. As a company comprised of driven innovators, its team and clients are top priority. Ai is honoured to be recognized as one of Triangle Business Journal’s “Best Places to Work,” for five years, in addition to being named one of 2023’s Most Promising Business Intelligence Solution Providers by CIO Review. Below is the feedback from one of its clients, NVIDIA, which deploys Wordsmith to augment marketing analytics directly inside their Tableau dashboard. NVIDIA found it essential to arm everyone with the tools to confidently make rapid business decisions. As LaSandra Brill, Head of Digital Planning and Insights at NVIDIA says, “Automated Insights’ Wordsmith has completely changed how our team interacts with Tableau. We can now ask the most pertinent questions directly within Tableau and receive real-time analysis from Wordsmith.”
Challenges in the Evolving IndustryThe current state of the data science industry leaves unlimited potential for growth and NLG is still a relatively unsaturated market. One of the biggest tasks the company faced was deploying emerging technology in such a new market with little historical insight. The technology industry is challenging in the best way because it is always evolving, it is always progressing, and companies are always working diligently to lead in innovation. The driving questions of who, what, why, how, and when are constantly in a changing state. The company is forever learning and re-learning about its audience, where Automated Insights fits best into the market, and how it can improve its services.
Future Path to SuccessAutomated Insights (Ai) is the creator of the world’s first self-service natural language generation (NLG) platform, Wordsmith. Natural language generation, a subset of artificial intelligence, transforms structured data into clear, insightful narratives. With Wordsmith’s role-based reporting, businesses are able to deliver the right insight, to the right person in real time using the simplest medium to understand: the written word. Automated Insights was established in 2007 and headquartered in Durham, N.C. In 2023, the company was acquired by Vista Equity Partners, a $30B+ private equity firm, making it the first natural language generation company acquired by a private equity firm. The company is moving forward with a mission to make the world’s data understandable. Automated Insights was founded primarily with a sports industry focus, generating sporting event recaps and personalized fantasy football narratives, before soon realizing that there was a deep need for this type of technology across various industries. Automated Insights now serves clients and partners spanning over 50 industries in more than 20 different languages. Its goal is to empower organizations and individuals with the ability to make faster, more efficient business decisions, regardless of their level of expertise or position within the company., is a technology executive and entrepreneur who has been successfully leading and growing companies since 1987. He earned both his bachelor’s and master’s degree in management from the Georgia Institute of Technology and remains active at Georgia Tech as an Alumni Mentor. Zionts is passionate about working with emerging technology, introducing it to the market, and maximizing businesses commercially to drive value and further innovation. As CEO of Automated Insights, he is building upon the established foundation to unlock exponential growth and technology developments. Zionts is also an Independent Board Director for Pivot 3, TEOCO, a TA Associates portfolio company, and Friends of the Earth, a Washington D.C. based environmental group.Automated Insights bridges the gap between the world of Big Data Analytics and Business Intelligence (BI) through NLG, which transforms large, structured datasets into human-sounding written analyses. Automated Insights’ Wordsmith authors data storytelling to reveal trends and provide role-based prescriptive reporting to make better decisions in real-time with otherwise challenging data. Using Ai’s platform, clients are given the ability to walk themselves through their data and are given what they need to know and what to do about it. Ai equips organizations with the most essential decision-making tool: an accurate, complete understanding of their chúng tôi amount of data the world produces and absorbs is growing exponentially. Disruptive technologies like Big Data Analytics, Cloud Computing, IoT, and Artificial intelligence aren’t just impacting today’s innovation, but are the driving force behind innovation. Each of these technologies are interlinked, feeding into each other and pushing each other to new heights. There is a mass amount of information and data being created by the IoT which has revolutionized the way the world consumes data. Because of this rapid growth, cloud computing has been driven due to the sheer volume and speed of information by the IoT. Artificial intelligence is being applied to a multitude of technologies to quickly enable the processing of Big Data Analytics, IoT, and Cloud Computing which drives better decision-making on all fronts, regardless of the industry.Automated Insights’ Wordsmith offers unparalleled customizability to empower companies with the capability to take complete control of the output, weave a cohesive story based on the entirety of their data, and ensure insights are more accessible and easily shared within an organization. Being the first to offer a self-service platform brings immense value to its clients and partners and enables them to structure, edit, and generate data-driven narratives without reliance on an outside analyst to provide insights. Key involvements that have helped drive the company’s innovation are Automated Insights’ business intelligence partners, including Tableau, MicroStrategy, TIBCO, Qlik, Interworks and more.Automated Insights is proud to be at the forefront of natural language generation, pioneering the world’s first self-service NLG platform. As a company comprised of driven innovators, its team and clients are top priority. Ai is honoured to be recognized as one of Triangle Business Journal’s “Best Places to Work,” for five years, in addition to being named one of 2023’s Most Promising Business Intelligence Solution Providers by CIO Review. Below is the feedback from one of its clients, NVIDIA, which deploys Wordsmith to augment marketing analytics directly inside their Tableau dashboard. NVIDIA found it essential to arm everyone with the tools to confidently make rapid business decisions. As LaSandra Brill, Head of Digital Planning and Insights at NVIDIA says, “Automated Insights’ Wordsmith has completely changed how our team interacts with Tableau. We can now ask the most pertinent questions directly within Tableau and receive real-time analysis from Wordsmith.”The current state of the data science industry leaves unlimited potential for growth and NLG is still a relatively unsaturated market. One of the biggest tasks the company faced was deploying emerging technology in such a new market with little historical insight. The technology industry is challenging in the best way because it is always evolving, it is always progressing, and companies are always working diligently to lead in innovation. The driving questions of who, what, why, how, and when are constantly in a changing state. The company is forever learning and re-learning about its audience, where Automated Insights fits best into the market, and how it can improve its chúng tôi companies are realizing the possibilities automated technology has for their organization and many early-adaptors are already seeing the fruit automation has produced. The company has seen a massive rise in interest around business intelligence, especially with the adoption of NLG platforms, to interpret data for better decision-making. Whether generating concise summaries of internal and operational business data, personalizing content for customers, vendors and clients, publishing content at scale or pulling key insights around competitive sales analysis, there are countless opportunities in which written analysis aids in understanding and decision-making across an enterprise. Automated Insights is focused on growing its product development offerings, fostering existing BI and system integrator partners, establishing new, enduring partnerships, and helping make the world’s data understandable one organization at a time.
Alteryx: Turning Data Into Extensive Business Insights
The platform enables any data worker to turn the tremendous potential of data into something real and actionable faster than before. Thousands of companies of all sizes experience the thrill of solving game-changing problems using Alteryx. With a vibrant online and offline community, everyone from business analysts to data scientists are loving their jobs again because of the Alteryx platform. The platform is flexible enough to harness the power of data for a competitive edge and deliver real business impact.
In the past twelve months, Alteryx has introduced several new solutions that help round out the end-to-end platform—from data discovery to prep and blending, to visualization and insights. These solutions include:
• Alteryx Connect—a data exploration platform that allows users to discover and collaborate on data assets, and workflow visualizations, etc. that are typically siloed across departments. It is an outgrowth of the company’s acquisition of Semanta in early 2023.
• Alteryx Promote: a component of the Alteryx platform that empowers both data scientists and citizen data scientists to deploy predictive models directly into business systems through an API and then managing model performance over time. It is the result of the Alteryx’s acquisition of Yhat, a Brooklyn-based data science company, announced in June 2023.
A Dynamic Leader Who Knows the Way
Dean Stoecker is the Chairman, Chief Executive Officer, and a Founding Partner of Alteryx. Dean’s leadership and motivational skills, along with his ability to create, communicate and realize a vision, are a driving force behind bringing back the thrill of solving to analysts and data scientists across the globe.
Alteryx is a 20-year success story with Stoecker leading the company through solid organic growth, three rounds of funding and a successful IPO in March of 2023 to deliver a transformational experience to customers of all sizes. Dean’s unique vision and strategy around bringing a code-free and code-friendly experience unleashes the line-of-business analyst from the menial and mundane to truly loving their work again.
2023 was a banner year for the company. In addition to being recognized as one of the top performers in the class of 2023 IPOs, Stoecker received the EY Entrepreneur of The Year® 2023 Award in the Orange County Region, which recognizes entrepreneurs who excel in areas such as innovation, financial performance and personal commitment to their businesses and communities. In addition, Dean drives a philanthropic leadership with the creation of Alteryx for Good, a program that brings the charge of solving real-world problems to non-profits, educators, and local communities.
Success Doesn’t Come Overnight
Dean has said, “You can live without an arm or a leg, but you can’t live without a heart; analytics is the heart of the enterprise.” Alteryx’s rise to success is not your typical Silicon Valley story. Based in Irvine, CA., the 20-year-old company was founded as SRC in 1997 before changing its name to Alteryx in 2010. Dean and the rest of the co-founders never took a dollar of venture money until 2011. Alteryx ultimately raised US$163 million in three rounds, when Dean saw an opportunity for growth and a broader vision for an IPO.
Client Satisfaction at the Forefront
Alteryx has earned the trust of thousands of customers around the world, ranging from many of the world’s largest and best-known brands, including Kaiser, Ford, and McDonald’s, to growing organizations such as Rosenblatt Securities, Veritix, and Consumer Orbit, who all want to use the power of data for a competitive edge.
Awards and Recognition
Alteryx has been honored by several organizations for company growth, customer satisfaction, and impact on the industry and community. A few notable awards include:
Changing Perceptions
Alteryx was traditionally known for its data prep and blending capabilities and the company is tasked with changing that perception. Its technology has evolved to a complete, end-to-end analytics platform, from data discovery to prep and blending, to visualization and insights.
Future Industry Predictions
Alteryx will continue to build its platform with the goal of revolutionizing businesses through data science and analytics. Below are a few predictions on where the data analytics industry is heading in general:
• Data science will break free from code-dependence. The company sees increased adoption of common frameworks for encoding, managing and deploying machine learning and analytical processes. The value of data science will become less about the code itself and more about the application of techniques. As analytics becomes more pervasive in organizations, and the number of data sources and statistical languages (R, Python, Scala, etc.) continues to expand and evolve, the industry will see the need for a common, code-agnostic platform where LOB analysts and data scientists alike can preserve existing work and build new analytics going forward.
• The CDO will come of age. Data is essentially the new oil, and the CDO is beginning to be recognized as the lynchpin for tackling one of the most important problems in enterprises today – driving value from data. Often with a budget of less than US$10 million, one of the biggest challenges and opportunities for CDOs is making the much-touted self-service opportunity a reality by bringing corporate data assets closer to line-of-business users. In 2023, the CDOs that work to strike a balance between a centralized function and capabilities embedded in LOB will ultimately land the larger budgets. Success will come to CDOs who put greater focus on agile platforms and methodologies that allow resources, skills, and functionality to shift rapidly between CoE and LOB.
Actionable Insights For Your Business With Data
Leading digital analytics experts share their examples of how to effectively use data and analytics to generate actionable insights for your marketing strategy
The number of data sources that are available is growing every day. For some of us, this might be a good thing, but for many digital marketers it brings up a lot of challenges to deal with. With increased data, it can be easy to lose focus, become obsessed by ‘vanity metrics’, and fail to generate actionable insights for your business.
McKinsey notes that surges in data caused by rapid digital disruption have not ‘provided marketers with a substantially better understanding of their customers, because their companies’ outdated data modeling isn’t able to capture these shifts with the necessary granularity and speed’.
So, while the innovators are leading the way with actionable analytics feeding their data-driven marketing, others become stagnant. In today’s competitive marketing landscape, this means losing market share, and customers, to your competitors. You don’t want that, that’s why we’ve got 10 tactics to help get you back on track.
Unlock actionable, practical strategy with the RACE FrameworkThat’s where our RACE Framework comes in. The RACE Framework empowers markers and managers to break down their customer journey across the 5-step RACE structure of plan – reach – act – convert – engage. Within each stage, we will guide you to set objectives, and measure the metrics and KPIs necessary to achieve your goals.
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Reporting vs. analysisThe job of a digital marketer and analyst is not to come up with a lot of fancy-looking reports that contain tons of data. In my opinion, data is just input. The insights you provide should be your most important output.
~ W.E.B. Du Bois
You need to answer what the numbers mean to your business and what action should be taken to improve the most important metrics of the business.
Turn data into actionable insightsI have worked with many companies that really got stuck here. Being able to collect the right data is one thing, but making it extremely useful requires a different skill- and mindset.
We’ve got marketing solutions to help you boost your use of data and analytics to inform your marketing strategy. Our marketing tools and templates are integrated across the RACE Framework, so you can apply a data-driven approach to planning, managing, and optimizing your customers’ journeys.
Find out more about how your company can benefit from Business Membership by booking on your free 1-2-1 marketing strategy consultation call. Our calls help you experience our bespoke services for businesses, kicking off by identifying opportunities for growth across your customer lifecycle using the RACE Framework.
Need a winning marketing strategy?Book your free 1-2-1 consultation to develop your new strategy with the RACE Framework
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Examples of actionable insightsLeading digital analytics experts share their examples of how to effectively use data and analytics to generate actionable insights for your marketing strategy
1. Measure the right thingsYou can’t optimize what you don’t measure. There is not a one-size-fits-all solution. Every business is different and should be treated in a unique way.
Let’s assume you run an e-commerce site. In this case, here are some examples of what you probably want to know:
Which channels drive the most conversions?
What are your leaking buckets (places where people leave your website)?
Whether people use multiple devices before purchasing your products?
What are the look-to-buy ratios for your individual products and product categories?
What landing pages need to be improved and in which channel?
2. Ask the right questions to stakeholdersGo the extra mile to answer all of your stakeholder’s questions. This means tapping into the stakeholderís aspirations and challenges by asking the right questions.
Examples of marketing insights that could add value for your stakeholders:
Outlining specific events and general trends to support your strategy
Critical environmental and competitor scanning, and market research
Commercial insights and ROI
You can easily waste hours of your time by getting lost in your data. And coming up with “insights” that are already known or not deemed important can be highly frustrating as well! By ascertaining your stakeholders’ expectations for actionable insights.
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3. Use segmentation to drive actionGo for segmentation if you want to take action on your data! By grouping visitors that have some attributes in common, you can start digging deeper. Choosing which segments to study depends on the business question you are trying to answer.
Identifying segments will greatly enhance your understanding of how your customers behave. You can use this information for setting up an optimization plan.
Digital analytics tools like Google Analytics come with a lot of built-in segments and provide you with all the freedom to customize them to your needs.
4. Use clear visualizations to convey your messageThe way in which you present your data will make a huge difference in the outcome. Do you remember these presentations that only include numbers and words? And this compared to clear visualizations that promote cognition instead of confusion.
It’s important to articulate a data story with as much what, how, and why behind it. This will turn your data into insights and profitable business decisions.
5. Discover the context of your data setMake sure to establish context for the data you are seeing. Examples of data contextualizing questions include:
What do these numbers mean?
Are they important?
Does it really affect the business?
And how is the data collected?
Data without context isn’t that meaningful and can actually lead to bad business decisions because of interpreting it in the wrong way!
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6. Build a solid optimization planUse the “Define Measure Analyze Improve Control” process (DMAIC) to improve your business. It’s one of the Six Sigma concepts you can directly apply in your situation.
Embracing the notion of continuous improvement, we continually aim for the identification and implementation of best practices and work to move closer towards perfect solutions for inefficiencies and imperfect processes.
In short, it comes down to:
Define the problem or hypothesis, stakeholders and scope of analysis.
Measure relevant data and conduct basic analysis to spot anomalies.
Analyze correlations and patterns, put your statistics and visualization skills to work.
Improvement based on insights and showing several options to explore.
Control the change by deploying (A/B) tests and monitoring KPIs.
7. Construct a great hypothesisFormulating these statements isn’t always easy, but you will save a ton of time wandering through your data and coming up with interesting – but not actionable – findings.
8. Integrate data sourcesCore Module
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9. Break down organizational silosA healthy organization is the foundation of everything. Communication instead of confrontation. Inspire, motivate and be curious about the data and the possibilities it has for your organization. Treat any obstacles first and improve the communication between the business and analytics leaders.
10. Don’t forget to hire smart peopleTools can collect data, but people – who understand the business – build insights. Smart people are required to find useful data, translate it into data-driven stories of useful knowledge – the insights. It’s a team effort where combining internal business experts with external analysts might be your best bet.
Strategize for actionable insightsEach of the tactics described above can help you fine-tune your action plan for turning data into actionable insights and profits for your business. You might want to pick a few tactics and first experiment with them. Find out what works best in your industry and situation and go from there.
Business Analytics In 2023: From Bi To Ai
Collectively, humans now generate 2.5 quintillion bytes of new data per day. The data we generate in a single year dwarfs every metric ever created between 2023 and the beginning of recorded history. In other words, the BI tools of the past can hardly be expected to keep up with today’s demands.
Not only is the overall amount of data increasing, the number of types of data are increasing, and the applications that store and generate data are increasing as well. Older BI tools can’t cope with larger volumes of data, and they also find it difficult to process data from new applications; it often takes a lot of manual adjustments to make an old BI tool fit a new app. As such, companies using BI tools may miss out on data-driven insights that are now available.
Exploring the Six Main Differences Between AI and BITraditional BI can no longer cope with the volume, variety and velocity of enterprise data. It’s time for new AI-powered tools to pick up the slack. But how is this new generation of tools different from what came before?
Data Collection and Integration
Within five years, 80 percent of your data will be unstructured. This data resists classification in databases, which makes it hard to tag, search and edit. With traditional BI tools, unstructured data sits in silos and is analyzed slowly, if at all. Data scientists spend about 80 percent of their time preparing this data before it can be analyzed.
With modern BI tools, preparation is faster and automatic. No matter what kind of data you need to analyze, these new tools can sort and categorize them within a single seamless data lake, making silos a thing of the past. These tools are self-service, making it possible for data scientists to begin receiving actionable intelligence in just hours or days, without involving IT operations.
Metric Coverage
Traditional KPIs – the ones that you set and research manually – only cover three percent of the metrics in play within your organization. If your KPI dashboards cover a hundred 2.5 qui, that means you’re missing 3,300 others. In fact, for a modern enterprise, 3,300 metrics would be on the small scale.
If something goes wrong in a user-facing application, the overwhelming likelihood is that it will go wrong in a metric that you aren’t currently covering. As long as the KPIs you’re monitoring don’t drop, you won’t be able to detect the error or outage until your customers let you know about it.
By contrast, AI tools know that it’s impossible for any organization to monitor all of their KPIs manually – so they take that problem out of your hands. No matter how many metrics your company generates, the orders of magnitude don’t matter. They’re able to ingest millions of metrics at a time and still provide instantaneous feedback if something goes wrong.
Thresholds and Baselines
Traditional manual alerting practices require data scientists to set thresholds for KPIs. When a KPI drops below a certain threshold or above another one, it sets off an alarm. Unfortunately, metrics tend to spike and drop unpredictably, even during normal behavior. Even if you set your baselines above and below those spikes, this discounts the possibility that abnormal behavior could still occur within the thresholds that you’ve set.
This practice also ignores seasonality, which is a normal variation in certain metrics that occurs on a daily, weekly or monthly cycle. To a traditional BI program, all seasonality looks like an anomaly, leading to a slew of false positives and false negatives.
Modern analytics platforms take a completely autonomous approach to baselining. They rely on machine learning algorithms to learn your metrics’ normal behavior and identify their baselines, eliminating the need for manual thresholding.
Detection and Alerting
Setting up traditional BI systems with manual alerting has a natural consequence – too many alerts. Alert fatigue is a real issue. In some disciplines, such as information security, personnel can experience over a million alerts per day. This makes it difficult for analysts to tell real emergencies apart from noise in the data.
With AI-driven reporting, there are no manual thresholds. The only alerts are “real” alerts – genuinely odd behavior in a metric. Even on its own, this behavior cuts down on false positives immensely. AI goes further than that, however. Modern BI tools give you the ability to alert on only the most severe deviations, allowing your response teams to focus only on what’s most important.
Root Cause Analysis
Anomalies don’t occur on their own. Using a traditional dashboard, you may be able to see an anomaly occur in one of the three percent of metrics you’re monitoring. Unfortunately, you won’t be able to see where else that anomaly shows up. This, in turn, means that it will take longer for you to understand where an anomaly is occurring and how to fix it.
By contrast, Autonomous Analytics reports on the full context of every alert. If two anomalies take place at the same time in related metrics, your alerting will reflect this. If these anomalies happen to coincide with a patch, an equipment failure or Black Friday, your reporting will reflect this as well. This makes it much easier to detect and mitigate anomalies.
Forecasting
Forecasting is different from anomaly detection – but with traditional BI, the same difficulties apply. It takes a long time to prepare data for forecasting, which is unfortunate when the business needs forecasts sooner rather than later. Since traditional analytics tools are constrained by the number of analytics they can ingest, your forecast will fail to consider all of the metrics that could potentially affect the business. In short, you’ll get a less-accurate forecast that takes longer to prepare.
With autonomous analytics, you get the forecast you need when you want it. Not only will autonomous analytics provide forecasts in seconds, the forecasts get more accurate every time you run them. The model will automatically compare its forecasts to subsequent events and then refine its conclusions based on what it got right or wrong – the longer it runs, the more accurate it becomes.
What Kind of AI do You Need?Autonomous Analytics programs eliminate the friction between data and analysis. Under a traditional solution, data doesn’t go where it should and needs to be massaged before it can be processed. It’s become too vast for humans or limited tools to process, and its metrics vary unexpectedly. In short, data is too large and varies too rapidly for the previous generation of tools to understand.
Leading solutions in the BI space are adding AI features to their existing products in order to keep up, but not every solution is created equal. Incumbents are adding their solutions piecemeal, without the completeness of green-field AI projects. Other vendors provide anomaly detection, but only for infrastructure data – which doesn’t provide the complete picture your company needs.
Only a fully autonomous anomaly detection and forecasting solution can provide you with the scale and speed you need to cope with the full velocity, volume and variety of your data. Whether you’re a seasoned data analyst or an inexperienced business user, these tools will help you get the actionable insights you need to compete in a changing environment.
Author Bio:
Amit Levi is VP of product and marketing at Anodot. He is passionate about turning data into insights. Over the past 15 years, he’s been proud to accompany the development of the analytics market.
Business Intelligence Vs Business Analytics: How To Distinguish
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In recent times, data has grown from simply providing information to driving change and influencing the core operations of companies. More and more businesses now seek data-driven solutions to solve everyday problems as well as thrive in a volatile market. In the world of data, we’ve also seen the emergence of two buzzwords: business analytics and business intelligence. While both have their salient features, the debate of business intelligence vs business analytics seems to be an ongoing discussion. So let’s dive into the fundamentals of both and understand what makes them so indispensable to organizations today.
What is Business Analytics?Amidst a changing business landscape, the meteoric growth of Artificial Intelligence and machine learning has led to enormous investments in big data. As noted by IDC analysts, in 2023 alone, businesses spent $215 billion on business analytics and big data. However, if the data is not adequately scaled to derive value, such investments will have no returns. This is where we need business analytics.
Closely associated with business intelligence and big data analytics, business analytics comprises a set of automated data analysis practices and tools that shed light on the operational processes of a business. In doing so, it helps professionals make informed decisions that have the potential to mitigate risks and optimize for the future. The result is a definitive data culture — one that recognizes the benefits, and practices, and encourages the use of data.
Continue Reading: What is Business Analytics?
What is Business Intelligence?Information and decision-making share a symbiotic relationship; this holds true for the domain of business as well. Business intelligence, put simply, has to do with the information. It is a technical infrastructure that leverages software to collect, store, and analyze data. Following that, it presents the data in accessible and user-friendly formats, such as dashboards, reports, charts, and graphs.
The USP of business intelligence is that it gives users access to various types of data — semi-structured and unstructured, historical and contemporary, and third-party or in-house. Transforming data into actionable insights on company performance significantly boosts its strategy and tactical decisions. Over time, business intelligence tools have become more user-friendly and intuitive, thus allowing individuals across industries to tap into their potential. In addition to smart decision-making, the tools are also crucial to spot key market trends, identify problem areas, and spotlight new business opportunities.
Business Intelligence vs Business Analytics: Key DifferencesVariances in business intelligence and business analytics reflect the trends in job growth, business language, organizational goals, and the size and age of an organization. Business leaders must consider these differences while investing or taking any significant decision that impacts revenue. The primary differences can be broadly categorized as:
Job Trends and LanguageDespite the overlap in their use, business analytics is a newer term than business intelligence. It also covers more ground than the latter than merely referring to a description of predictive and statistical tools. The increasing growth of analytics also indicates higher employment rates in the field as compared to business intelligence.
Size and Age of OrganizationThe size of an organization is a determining factor in its use of business analytical tools or business intelligence. The latter is a good fit for enterprises of all proportions, even smaller companies that want to incorporate data into their present operations as well as predictive plans. Furthermore, newer companies tend to prefer analytics to business intelligence. This is especially true for startups more than established brands that are more interested in employee performance or organizational processes. However, at the end of the day, a combination of the two is the best way to go and is greatly preferred.
Focus on the Present Vs. the FutureOne of the key distinctions between the two is their temporal approaches. Business intelligence relies more on historical data to determine how a company should function in the present day. On the other hand, business analytics concerns itself with future predictions and planning. Established businesses that want to identify pain points and improve efficiency often opt for the former. But for those looking for a change in their business model, analytics may be more useful.
The bottom line, however, is that all businesses have both a present and a future focus. The best case scenario, therefore, would be to combine the two into an approach that optimizes existing strategies and also reserves space for innovation.
How to Improve Your Business Intelligence SkillsProficiency in business intelligence demands, like all fields, a blend of hard and soft skills. Improving one’s competencies is no easy task. But with the emergence of numerous resources, such as online courses, bootcamps, books, and virtual seminars, we can now sharpen our skills in a way that is flexible and affordable. However, it’s important to remember the core skills that comprise business intelligence.
Data Analysis Report GenerationThe chances of getting hired are far higher for the one who can take raw data and interpret it to draft comprehensive reports to boost company performance. This is useful for clients and stakeholders alike as, essentially, it works as a means of communicating information clearly and fluently.
Practice Using Data DashboardsAs the primary information management tool in business intelligence, data dashboards monitor, evaluate, and reveal Key Performance Indicators (KPIs), important metrics, and particular data points. As a business intelligence professional, not only is it crucial to know the ins and outs of how dashboards function but you must also know the differences between various dashboards to select the right one for a specific data-related job.
How to Improve Your Business Analytics SkillsAs curiosity-driven as business analytics professionals are, certain technical skills go a long way in a tech-driven world. Such skills include:
Data literacy
Competence in data collection
Statistical analytical skills
Proficiency in data visualization
Improving business analytics skills means embracing digitization that drives innovation and large-scale change within organizations. While a thorough grasp of the fundamentals is essential, it is vital to know how a digital strategy, coupled with technical know-how, can transform the larger mechanism and supply chain ecosystem.
The growth of business analytics over the last decade has led several resources to emerge. In addition to books, seminars, and boot camps, online courses have gained immense traction as they are flexible and affordable. They often offer certification from some of the most renowned institutions in the world.
We have witnessed revolutionary changes in the space of business due to digital transformation, and the impact of data is hard to ignore. It has made information accessible and actionable in real-time and has been a crucial driver of growth. In the face of unprecedented change, political crises, environmental disasters, and a volatile market, both business analytics and business intelligence are critical to saving businesses and, in turn, human lives. Changing the world was never easy. But the two fields, hand in hand, hold the immense promise of a brighter future.
By Deyasini Chatterjee
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