Collecting and refining data is at the forefront of advancing business initiatives, and many companies are already taking steps to do this. Recently, data labs have gained popularity as a means to solve business problems with timely, data-based solutions. While the practice of collecting data is nothing new, data labs are changing the game as far as data usage and discovery. The pressure is on executives to stay on top of data trends and implement them for their own business practices.
What is a data lab?
A data lab is a designated data science system that is intended to uncover all that your data has to offer. As a space that facilitates data science and accelerates data experimentation, data labs uncover which questions businesses should ask, then help to find the answer.
The Definitive Guide to Data Integration now.
The business value of a data lab
Data labs offer advantages that can improve operations and uncover valuable business information. These advantages include:
- Managing multiple data science projects: Since data labs are designated systems separate from a data lake, center, or warehouse, they have a larger capacity for maintaining various data projects at once.
- Data labs, especially when used in conjunction with the cloud, allow for easy scaling up or down depending on the needs of the company. So all projects get done, no matter the workload.
- Generating reliable, refined outputs: Executives can enjoy greater confidence in business intelligence with the sophistication offered by a data lab’s outputs. Data labs create processes that generate stable outputs to better inform business decisions.
- Positioning the business as a thought leader in its industry: Data labs yield innovative data insights that help to push the envelope in developing business sectors and that keep companies on the cutting edge in their fields. With a data lab, you can tackle the large problems and come up with unprecedented solutions to drive your business to the top.
Though the distinct business advantages will vary between companies, it’s clear that data labs add certain business value to operations as a whole.
Build a True Data Lake with a Cloud Data Warehouse now.
Data labs and the cloud
Cloud technologies enabled the evolution of data labs by allowing for increased versatility, remote access, and secure data monitoring. As more businesses bring their big data to the cloud, they are also using the cloud to analyze and experiment on data.
This trend forecasts that progressing cloud technologies will drive the advancement and cost-effectiveness of data labs. With the agility, scalability, and predictable operational costs offered by the cloud, data labs can grow and expand based on business needs and free up funds for other critical business initiatives.
How to set up a data lab
Like organizing any key operation, setting up a data lab is an involved and complicated process. While it can be intimidating, businesses can successfully establish their own data labs by taking the right steps.
Step 1: Clearly outline business objectives
This includes sufficiently defined analytics goals and identifying key performance indicators (KPIs) based on those goals. At this stage, it’s also important to outline the specific responsibilities of the data lab, as these responsibilities will help to inform how to set your budget, and what qualities to look for in data labs resources
Step 2: Set a budget
Based off of business objectives, data lab responsibilities, and goals for the data lab, outline a budget that fits the needs of your company. Weigh your options for what to implement; like whether you’ll use a data warehouse or a data lake to store data, what kinds of hardware and software you’ll require, and which cloud provider to partner with if necessary.
Step 3: Make sure you have the right resources
Before a business launches a data lab, they need to locate the right resources. While big data labs offer incredible data innovation, at their core, they are run by highly trained and qualified data professionals.
Every data lab needs at least one:
- Data scientist: As the person responsible for creating and refining models that can identify new areas of data to explore and make data predictions, the data scientist plays a pivotal role in the success of a data lab.
- Business analyst: A business analyst supplies the soft skills necessary to understand business entities and their customers. By working in tandem with the data scientist, the business analyst can help direct the team toward high priority data projects and help decide which problems to solve first. This person is also responsible for applying data results to business initiatives and objectives.
- Data engineer: A data engineer works with the raw data and then organizes and transforms it so it is usable by the data scientist for creating program algorithms that accomplish project goals. They are also tasked with making sure the data is accurate and relevant, and that their operation complies with governance rules and regulations.
After laying the right groundwork and taking steps to obtain expert resources, businesses can begin to establish a data lab that will open doors and drive innovative practices.
Challenges of running a data lab
The many ins and outs of building and running a successful big data lab are not without challenges. Common challenges include:
- Finding the right personnel: Finding and bringing in highly trained and specialized data professionals is an obstacle for most businesses. More importantly, the resources you do bring in must work well together for the data lab to be successful.
Luckily, many data integration providers offer staffing as a service solutions, and can place skilled professionals across many sectors. Some data integration partners provide training for personnel, which allows businesses to establish and bring their labs up to capacity in a timely manner.
- Ensuring all programs in use are compatible: Avoid adopting incompatible software platforms within your data lab. A trained consultant can assess your current setup, identify areas of improvement, and offer best practices.
- Keeping data safe: Big data is precious, and there are people trying to obtain that sensitive information for personal financial gain. It’s vitally important to keep data protected, but when housing large sets of information, it can be difficult to ensure it remains secure at all times.
Cloud-based data security offers constant monitoring and remote access protected by intricate authentications. So even if data lab professionals aren’t continually monitoring their data, you can rest assured that a trained professional is.
Data Lakes: Purposes, Practices, Patterns, and Platforms now.
Getting started with a data lab
“Data lab” is quickly becoming a buzzword across industries. Businesses are looking to make smart decisions about their futures that are based on accurate, highly sophisticated data sets. These systems offer competitive business advantages and help to position businesses as thought leaders in their fields.
With cloud technologies, data labs generate even greater agility and innovation by allowing for increased access and security monitoring. Though not without its challenges, developing and running a data lab could be the very thing needed to push business operations to the next level.
Start by getting key stakeholders in a room together to outline business objectives. When you’re ready to move forward, make sure your data is ready too. Talend Data Fabric is a cloud-native, unified suite of apps that integrates and manages all of your data. Get more information and learn how Talend’s training and consulting services can get your data lab set up properly.