Unleash the Power of Data: Maximize Business Impact With DataOps | DataOps Day

Hi, I'm Jennifer Glinsky, director of product Management in BMCs Innovation Labs. In an error defined by data's exponential growth, businesses are driven by a common ambition to become data-driven powerhouses that make strategic decisions with precision and foresight. Yet this journey is not without its challenges.

The path from data to actionable insights can be obscured and investments need to be meticulously justified. Today we embark on a voyage of discovery to unveil the transformative potential of DataOps, a methodology that empowers organizations to estimate and elevate the business impact of data-driven strategies. Did you know the global big data and analytics market is projected to grow to over $307 billion this year?

In this era of unprecedented data growth and technological advancement, organizations find themselves swimming in a sea of information. The promise of insights and opportunities lies within this vast ocean of data, but the question that looms large is how do we NA navigate? But the question that looms large is how do we navigate through this data and extract the value that drives meaningful impact?

We are embarking on a journey to uncover the keys to turning data into insights that lead to tangible business success. To bridge this gap from data to impact, we introduce data ops, which blends agile methodologies with automation to orchestrate data management. Data ops empowers organizations to optimize business processes, talent allocation and their overall data flow, ultimately translating to a net benefit for businesses.

So let's begin with the need for data ops. Why are we concerned with it and why are we here today? So we know organizations are investing heavily in data and analytics and AI projects.

Well, unfortunately, a significant number of these projects fail to meet expectations. Only 40% of firms are managing data as a business asset. In order to monetize data, you need to be able to get it from the sources that create it into the hands of people that can do something with it, people that can take action based on it.

And what we're seeing is almost half of these big data projects to do just that are failing. 19% of firms consider themselves to be data-driven, which is awesome. It means that they're using data to make decisions, but if half of their big data projects are failing, could they be using bad data to make those decisions?

Why then are so many of these big data analytics and AI projects failing? Well, there are several reasons why these programs could fail, starting with the exploding amount of data and the increase in the number of data sources due to newer types of data or more complex business flows that make simplification difficult or challenges into in deploying to production at scale with high expectations on flexibility, speed and customization. A lack of collaboration doesn't help either, especially when different teams are involved in the projects, nor does process mismatch where traditional data management technologies and approaches don't really match up well with new advances such as ai.

We're seeing a limited talent pool in the industry since data analytics and AI projects require both internal domain knowledge as well as deep technical skills. These data analytics programs require significant investment and there's a risk of becoming overly focused on the latest tech at the expense of delivering real business value. How many of you have been asked how can we use chat G B T or AI in X, Y, or Z where it's a very simple process like mailing your letters at the bank or post office or conducting your general day-to-day businesses?

Not everything needs AI or analytics, and sometimes we overcomplicate things just because it's shiny and new and exciting. Lastly, a unclear approach to measuring success doesn't really help either because many of these benefits are often observed in other teams for these foundational initiatives, these are certainly hefty problems to solve. Which of these would you say is the biggest challenge in your team?

Which of these challenges are you witnessing in your organization? Personally, I've seen an exploding amount of data in many environments, so let's take a little look at that challenge for a moment. Businesses worldwide have been on a data collection spree driven by the staggering projection that will double the amount of data generated in the world in just four years, from 2022 to 2026 to around 221 zettabytes of data according to I V C.

Now, I will confess, I don't even know what a zettabyte is, but it sounds very, very big and just the fact that we're going to double all the data in the world in four years is astounding, but admits this data frenzy, a fundamental challenge emerges how to transform this data deluge into actual insights. It's the same challenge we've been faced with is just gonna get harder. The journey to meaningful insights begins with clarity.

First of all, do you know what data you possess? Is it the right data? And more importantly, is it yielding real value for your business?

These are key questions I would encourage you to consider as you begin your data ops journey. The second reason analytics and AI projects fail is because organizations face challenges in operationalizing data pipelines at scale. This is just as important as that first reason.

A few data pipelines might be easy to maintain manually with only one or two small problems, but as you scale up those problems quickly become unmanageable. This is when you need to introduce data pipeline orchestration to keep things under control. What happens then when you overcome these challenges?

B M C solutions like Control M are supporting industries around the world to orchestrate and manage data needs from mainframe to the cloud. And we're seeing BMCs customers like Raymond James, I n g, Navistar and more making a difference in the world around them through their successful analytics and AI projects. For example, they are saving more lives by analyzing hospital operations data.

They are building tools that help students and they are optimizing commercial space to make room for cute new shops and cafes. In turn, making someone's morning just that little bit brighter with a cup of tea and a biscuit. Of course, this is only a tiny taste of all the ingenious ways DMC's customers are improving the world around them through analytics and ai.

How then can organizations get more of the programs just like these in two production? The answer lies in data ops. Data ops has emerged to help organizations overcome some of these challenges and increase the number of successful data and analytics programs out in the world.

What is data ops then? Data ops applies agile software development best practices to managing data pipelines. This helps engineering and data teams deliver the right data to the right people when they need it.

BMCs Quest for knowledge led us to commission 4 5 1 research part of s and p Global Market Intelligence to survey 1,100 data and IT professionals around the globe about the value of data and data ops. In particular, the insights gained from this extensive study show that organizations with higher data ops maturity experience greater data efficacy, provenance and availability. The correlation between data ops methodology and business objectives is undeniable.

We've discussed some of the reasons analytics and AI projects might fail, and we've introduced data ops, so let's consider an example to illustrate these concepts. Consider this scenario which illustrates some of the challenges data professionals face when they try to manage data pipelines manually and show us why and how DataOps has emerged via valuable tool. I'd like to introduce you to Jane here.

She is a data analyst who's been asked to build a report to show how segments of customers changed their spending over time. Also, this is based on a true story. So to create this customer spending report, Jane builds a SQL query into a data warehouse to get revenue data by customer.

She then creates a Python script to aggregate everything by the date of a customer's first purchase and she imports the data from all of these fun scripts into a reporting server so that she can load her report. Finally, she builds a scheduler to run the data pipeline and populate her report every Monday morning. Things are looking pretty good for Jane and really this is all she thinks about when she thinks about her data pipeline.

She thinks about the areas that she touches, which is how most people think about their pipelines. However, it's not the full story. Jane's pipeline actually begins with data coming from three different source systems from customers, sales and finance.

Considering a world without data ops where Jane is managing her pipeline manually. Small changes can have large undesirable impacts. For example, say the finance team changes something in their database.

Say they add a new category for revenue. Well, traditionally, Jane's not gonna necessarily know about that. Her calculation script's not gonna know it's gonna miss this new category and it's gonna impact her final report.

When she finds out about the change, she'd have to go make multiple versions. Now she's managing a bunch of different files. 'cause what if that change broke it?

She'd need to back it up. Things start to get very messy this way. Also, in the worst case, Jane doesn't notice this reporting issue right away and Jane stated, consumers like her boss or a customer are the first ones to notice there's something wrong with that revenue population in her report.

They're not really sure what's going on, but her report doesn't match her the other reports they get. So now they don't really trust Jane's reports and maybe they're not, they don't really trust Jane. This is not a great day to be Jane, and I'll tell you a secret, I have been Jane before and it is not fun.

In another case, Jane might notice that the problem exists before anyone else catches it, but that's also not great because think of all the people who've been using her reports thinking it was right, who knows what kind of decisions they made based on that information that was inaccurate out of date or just flat out wrong at that too. The whole messy file management issue we just talked about and things start to get very undesirable very quickly for Jane. So we need to reframe how we think about data pipelines and the flow of data to consider the full path of data through an organization, and we need a framework for managing those enterprise wide data operations and transformations.

This is where DataOps comes in. It is a framework for you to do just that. By implementing DataOps principles, including pipeline automation and data observability, Jane can orchestrate her pipeline and proactively manage changes to keep things running smoothly.

We know DataOps brings the best practices from software development and IT and applies them to data pipelines. What does this look like? This includes automation and orchestration, which allows you to efficiently move data from different sources such as structured IOT or streaming data like we see on the left side of this diagram all the way through data pipelines to consumers of data on the right side of this diagram, including analytics, data warehouse, visualizing reporting, or my personal favorite data science.

Unfortunately, as we saw in our example with Jane, manual efforts alone cannot keep up with managing the immense volume of data generated in enterprises today. This is why many organizations are implementing data ops best practices to bring automation and observability and more to their pipelines. As I'm sure you know, working with data is not without its challenges and the following ones are good ones to look out for four or five.

One research's study revealed common issues, hindering progress towards a unified view of data. Then cover that there are persistent struggles in data quality, automation and culture, specifically meeting complex needs, automating processes. There are many data quality problems and data silos.

In system interoperability poses a challenge. First, a key reason why organizations need data ops is because the data pipelines required to feed data and analytics and AI projects are so complex. Believe me when I say the sheer number of tasks and connections involved in a data pipeline can be intimidating.

Take this image for example. It's a real data pipeline with tons of different data tables and jobs across different environments. They are transforming data, they are moving data, they are filtering and uh, joining data.

They're doing everything you can can do to data and this is only a single data pipeline. This isn't even the biggest data pipeline in the world. This is just a very typical data pipeline.

When you scale up to larger, more complex organizations, navigating and managing data pipelines becomes tremendously complex and difficult to solve. A large company could have 20,000 data pipelines with 5,000 data scientists or ML engineers around the world consuming thousands of data sources. All of this complexity provides endless ways for data pipelines to break, especially when changes are introduced by different people managing different parts of the process.

The demands of a 24 7 business model and the internet of things and edge computing. All of these technological advancements also necessitate seamless data collection and oftentimes streaming capabilities just adding to the complexity of data pipeline needs. Secondly, B M C recognizes that data pipeline failures are often caused by a change to the data or workflow and people need a way to manage them and catch them or prevent them in the first place.

Ultimately, Jane's problem with her pipeline situation was that there was a lack of automation and observability in that pipeline over reliance on manual methods, hampers efficiency and innovation. Unfortunately, doing everything manually just doesn't scale. Data quality is also a top challenge in data management today.

Inaccurate and outdated information undermines trust in decision making. Like we saw in Jane's example. Imagine you are a data data consumer.

Would you use a report if it was inaccurate once a year, once a month, once a week even? What if it showed wrong data? Perhaps it's out of date.

Perhaps someone typed in a typo and it's just the wrong number altogether and you didn't know when it was accurate or when it was corrupted or wrong. If I was in that situation, I would do my best to proceed with my job with the information I had at hand, but knowing that the data I had was often wrong or invalid would make me very hesitant to act on it. I wouldn't be very sure of the decisions I was making, know the direction I was taking, and it would definitely impede my progress and slow me down.

I would have to be looking at other data points or trying to validate my ideas elsewhere, knowing that I couldn't rely on just this data. Would you behave differently? Unfortunately, without data ops, without data observability, many people are in this exact situation and they don't even realize it.

System interoperability and data silos are our fourth challenge on our list to watch out for fragmented data hinders holistic insights and efficient operations. Different pieces of information fill in the bigger picture, like pieces of a puzzle. With disparate data silos, you end up missing pieces of that puzzle and with challenges in system interoperability, you have trouble getting and adding those puzzle p.

With challenges in system interoperability, you have trouble getting and adding those puzzle pieces to the picture even if you find them. It's not all challenges though. Fortunately, DataOps brings innovative solutions to help us maximize value from our data.

That is what we are most concerned about getting true business value from all the data that we are collecting, storing, and processing because let's face it, it's not cheap to do all those things. Costs us money to collect store and process data and invest in analytics and AI projects, and we want to maximize that return on that investment and really get all the value out of that data that we can. Turning challenges into opportunities requires innovative solutions and these challenges we just reviewed illustrate why automation and observability are critical to the success of DataOps initiatives.

You need automation to help reduce errors in the first place and you can use data observability to help you catch the issues that do make it into your pipeline. We can see here where automation and observability fit into our data ops diagram that we saw earlier. You can see that orchestration really touches all parts from the data sources through the data pipeline to the data consumers where data observability is really critical and useful in the data ops pipelines themselves.

Data ops is a transformative force applying agile engineering and DevOps best practices to our data management and helping us solve some of our biggest challenges today in those areas. Through DataOps, collaboration among DevOps teams, data engineers, data scientists and analytics teams, fuel data-driven insights. Three key pillars of DataOps impact are one, data quality, two business insights, and three, innovation and efficiency for data quality.

Establishing guardrails for data identification, collection and analysis enhances your data integrity. It makes your data more reliable, more usable, more trustworthy, all the things that you want your data to be. Uncluttering, data enables precise insights, driving revenue, generating activity, and data ops brings cost savings from streamlining data processes which fuel innovation initiatives that can drive growth.

Who doesn't like saving money? I love it. To unleash the full potential data, organizations must cultivate a culture of democratized data, underpinned by unified views, automated processes, and comprehensive data management.

You want to develop a holistic data approach, a 360 degree view of data assets underpinned by a data ops methodology paves the way for data maturity. Now of course it might be easy to say you need a holistic data approach, but implementing that can be a little hard and oftentimes I found that it requires a culture and technology shift to adopt this holistic approach that marries agile methodologies with automation to drive data-driven business outcomes. Now, in many organizations, if you're already using agile methodologies or you've got some DevOps practices or principles in place, it can make it easier to get started on your DataOps journey as well.

These are recognizable to many teams and you can understand how the benefits would transfer over from traditional IT domains into data management, so it's less of a cultural shift if you can show those types of benefits like we've explored here today. Also, a lot of people like getting their jobs easier, faster, smoother, so when you are able to explore the benefits of DataOps in particular, reducing errors, streamlining processes, and reducing costs, it can be, these can be very helpful tools in motivating that cultural shift as well. Remember at the start of our journey today, when I mentioned that many analytics and AI projects fail to meet expectations, it was around half.

Unfortunately. Well imagine all the cool things that could be if the other half of those analytics and data and AI projects were successful, the impact could be as big as saving your aging parents from cancer or as small as discovering your next favorite band and data ops will help us get there. Our voyage through the realms of data ops and data-driven excellence has revealed a landscape teaming with potential.

By embracing DataOps and leveraging automation, information orchestration and observability, organizations can steer towards unparalleled business impact. Together, let's seize the power of data to unlock a future of possibilities. com to join the ops revolution.

Thank you so much for having me today.