In an era where data is king, and big data analytics and related tools are the king of kings, enterprise big data analytics is no longer a differentiator, but a password at the door for some industries.
Enterprises in industries like banking, energy, transportation and others rely on big data to not just keep a competitive edge in their markets, but even tread water. Up to 60% of businesses have incorporated big data as recently as 2018, a number that is sure to have only increased recently.
Implementing big data into an enterprise should not (and really, can not) be taken likely. It needs to be strategic, focused and thorough.
In this article, we’ll explore the benefits of integrating enterprise big data analytics, implementation models and strategies, methods for developing an effective enterprise big data lake and skills required by enterprise big data professionals.
Adding big data analytics at the enterprise level has unique benefits. For enterprises large enough, it’s a necessity just to deal with all internal data. But, if your company is large enough to meet the definition of enterprise, it’s more than likely that your competitors are that size too. That means that there’s also more competitor data out there to be had to optimize your operations.
Here are three key benefits of implementing big data into an enterprise:
Blending internal enterprise data with external sources gives a more informed perspective on internal metrics and key performance indicators (KPI).
When focusing specifically on business operations, using big data gives a better perspective on its place among its competitors. You can harvest all your enterprise data analytics, all your competitors’ and additional market data to forecast your future KPIs. If you can get specific KPIs from competing businesses it gives a direct comparison. If not, any auxiliary data that can be found can potentially be used to project those KPIs.
Social media harvesting can be used for sentiment analysis, which can also be used to determine position compared with competitors.
Big data can tell you that the aspect of your business that you thought was lagging is just the market cap for that indicator, or something you thought you were excelling in is actually at the back of the pack.
Better Customer Understanding
Using big data lets businesses better understand their customer base. Not just who they are and where they are, but why they are customers as well. Multi-dimensional data on customers lets businesses dig deeper into how they think, why they need a product, why they chose (or didn’t choose) a specific brand over another.
Customer analytics can enable informed changes to marketing, sales, even packaging or the product itself. Blending it with market analytics lets businesses project customer needs, identify product life cycles and ensure they provide enough supply to match demand while reducing surplus.
It also allows for customer satisfaction compared with competitors, letting businesses figure out who in their market is making their customers happier and why.
Seeing the future
OK, not really. But it does let you make more informed predictions and be more confident in the actions you take based on those predictions. And if you’re not using big data to project out what’s ahead, you’re probably behind: nearly 70% of IT professionals use predictive analytics in their workspace as of 2019.
As we’ve noted, implementing big data into an enterprise isn’t as simple as sending your IT person on a two-day task. It’s a long, convoluted process.
It’s so complicated and difficult that as many as 87% of data projects, big or small, never even see production. Of the ones that do manage to get started, up to 60% failed as of 2015, the latest data available.
Why does that happen? The Center of Applied Data Sciences outlined several points for the extremely high mortality rate of big data projects:
- Leadership Troubles: One of the most significant issues is that executives do not realize how involved they must be in the big data process, and are often unprepared to give such a commitment. This leaves data projects to wither away in waiting until they eventually turn to dust.
- Poor Communication: The two-way channel between the project managers and executives needs to be transparent and thorough. Around a third of the time, needs or expectations are not concisely conveyed, leading to the project crumbling.
- Lack of Skills: Simply put, sometimes, enterprises don’t have the capabilities to reach their aspirations. While some personnel selected specifically for the project are willing and able, “dataphobia” still runs rampant, impeding any ability of implementation.
- Ambitious Intentions: Sometimes, enterprises set their sights too high. It’s not a lack of talent or resources, but they simply expect or want to do too much. Big data can be a costly endeavor that requires a lot of time, energy and resources, and asking the world simply isn’t feasible.
Other reasons for failure include failing to understand the financial commitment required and hitting the end of the runway on the project. Others simply don’t understand what implementing big data truly means, and just hear the buzzword and want it for their business. They don’t get the task they are giving their employees, dooming the whole thing up to failure from the start.
So what’s the best way to become one of the lucky few to pull through and effectively establish a running enterprise big data system? Plan.
Gathering the executives, data scientists and project managers so everyone involved can concisely and thoroughly explain what they need and what they want to accomplish. Some topics that should be covered include:
- The problem to solve
- The resources available vs. the resources needed
- The availability of market/external data
- The timeline to fully implement the project
- The involvement required for each party
- An outline for the steps required
- Specific approaches to each of the four components of big data (Volume, Variety, Velocity and Veracity)
These are only a few of the issues involved, but you might see how it can dive deep into the theoretical and hypothetical quickly.
Developing a big data enterprise architecture and a big data strategy and being thorough with the planning and pre-production process is essential to success.
Big Data Strategy
A big data strategy outlines the overall process for intaking, processing and analyzing data on a high-level to demonstrate how to achieve the goals set by the enterprise. It’s where all the issues of master data management and data governance get defined.
It is essential to solving the issue of poor communication, giving each person involved a roadmap to the end goal of using data to better an enterprise. According to SAS, thoroughly developing a data strategy is helpful in several ways, including priority setting, avoiding collecting unnecessary and cumbersome data and improving efficacy through getting everyone on the same page.
A strong data strategy will provide the framework for filtering in data and making it all cooperate, even with enterprise data. It will streamline the process of acquiring, organizing and analyzing big data.
Big Data Architecture
Data architecture is a more granular approach within the strategy of exactly how the specific data will move through the process, from its ingestion to consumption. It is a subset of the data strategy that is handled almost exclusively, like the data scientists and professionals. It has three components:
- Outcomes: The final, finished product of the analytics process
- Activities: The specific actions taken in the process
- Behaviors: Who will be responsible for taking actions and how they’ll do it.
To the untrained eye, the above graphic can be daunting, filled with unfamiliar jargon and confusingly organized. But, it’s an essential simplification to coordinate a big data project.
There is no one-size-fits-all for data architecture. It’s very likely a company looking to integrate big data will want the capabilities of analytics in real time, but it’s not a necessity. However, there are a few skeletal pieces that are required in each, such as a big data platforms, scalable software for analyzing, and some way of addressing security. As more computing efforts move to the cloud, virtualization and instancing is becoming increasingly essential, as well.
To fly into the world of big data without a strategy and architecture is to fail. Thorough planning and understanding exactly what it means to undergo a big data project for an enterprise is paramount to preventing it from failing.
Enterprise Big Data Lake
An integral step in the process of big data analytics is the creation of a big data lake, the term for the storage place of all ingested data after it is cleansed and prepared for analysis. It’s all the external data combined with the enterprise data warehouse or lake.
Having an effective and organized data lake is pivotal: if the aggregation of data is incomplete, the resulting analytics won’t be accurate. If it has unnecessary, irrelevant or redundant data, it will slow the analytics or corrupt them. If the data pieces aren’t formatted in a way that can be read by the analytics applications, it will be unusable until it is translated.
When designing a big data project, things like speed, data structure and security all need to be accounted for in the building of the data lake. It’s where all of your data is going to live, and it needs to be done correctly. The alternative can be devastating to an enterprise: the cost of a failed big data project is vast to begin with, but working with incorrect data and making key decisions based on it can be a death sentence.
Enterprise Big Data Professionals
In this whole process, the data scientist is the most important figure. The executives know what they want and will pay to supply the resources and the project manager will have some technical idea of how to accomplish it. Still, the big data professionals are the ones with their digital boots on the ground, getting the work done. So what should enterprises look for in big data professionals?
First and foremost, employers need to look for workers with a deep understanding of coding. Most big data applications utilize R or Python, or a combination of both. While a lot of products have minimal-coding options, being unable to customize and build those functions on the backend will greatly pinhole the scope of tasks an enterprise can accomplish through big data. This includes being able to work with machine learning and AI, and an ability to compose data visualizations through coding and data warehousing.
Next in line is a grasp of statistics, data manipulation and analysis. Being able to work with data and pull the between-the-lines semantics from it is the whole reason for data analysis. If you’re not trained for that, being a data scientist probably isn’t going to be for you until you can.
Other necessary skills include communication and business knowledge. As we noted earlier, poor communication is a big data killer. Being a big data enterprise requires not just the skills to build an integration, but the ability to function within a team to do so.
In this article, we explored what it takes to become a big data-driven enterprise. We outlined the planning process from building strategies and architectures, the types of employees needed to do so, and the benefits of making it all work. Adding big data to your enterprise is high-risk and high-reward. If you can pull it off, it can be a game-changer for your company, but a lot of attempts to make it work fail.
If you’re beginning your exploration into the world of big data, SelectHub can get you off the docks. Our crash course and explainer are great places to dip your toes in the water. If you’re looking to go a little deeper, our features and requirements article might be the reading for you. If you’re trying to implement big data, our comparison report can give you a customized list of vendors that best suit your needs, or you can call us at 855-850-3850, and we’ll steer you in the right direction.
What was your enterprise’s big data implementation strategy? How successful were you? What challenges did you experience? Did we miss anything in this article? Let us know in the comment section below!