If you've joined a new company, the first 90 days are critical to your success. You need to respond to new data sources, data types, and applications efficiently. The ever-growing volume of data can make that challenging.
During this early stage of your job transition, you need to impress, and you can do this by re-thinking your data engineering strategy. In a world where data is more vulnerable than ever and legislation over data collection is more stringent, the right strategies will help you excel in your new role in just under three months.
Here are a few things you can do. Feel free to explore the topics you think can be most helpful in your role:
Identify the Challenges
There will be challenges in your first 90 days:
- Silos: Service environments or legislation will prevent some data engineering processes, and this will inhibit data science.
- Processes: Slow, inconsistent data engineering processes will slow down your entire organization.
- Technology: The wrong tools will prevent data engineering from being successful and hamper digital evolution.
- Culture: A work environment that doesn't value data engineering will make your life difficult.
Find solutions to these potential outcomes early on and include them in your strategy. Lay down how you will overcome these challenges so you can carry out your data activities.
Don't Waste Time
Ask yourself the following questions on day one:
- What data should you keep?
- What data should you lose?
- How will you structure your data? In a data warehouse? A data lake? An event stream?
- Where will you store data? In the cloud? Virtualization? A federation? Hadoop?
- How will you integrate data? ETL? Cognitive profiling?
- How will you cleanse data?
As you progress through the first 90 days, you will come across many challenges, but you soon formulate an effective data engineering strategy.
Think About How You Will Deliver Your Data Strategy
How you deliver your strategy will impress colleagues in the earliest stages of your job transition.
Your strategy should:
- Have collaboration features so multiple collaborators can make comments and edits
- Be in an accessible format so engineers, scientists, and executives know where it is and what's on it
Then think about what to include in your strategy. What data is crucial for achieving performance in your organization?
Think about the purpose and scope of your data:
- What will your data achieve?
- Does data complement your business strategy?
- How much is your data worth?
- How will you measure the success of your strategy?
Data engineering is a continuous process that will evolve over time, but identifying your long-term goals will help you prioritize processes and optimize resources.
Once you've recognized the basic structure of your strategy, think about the specifics of data engineering and development in your new organization.
- How frequently will you prepare data?
- How frequently will you transfer data?
- What data transfers are the most necessary?
- How closely will you work with data scientists?
- How often will you archive data?
- How will you deal with data silos?
Here, you are joining all the dots between your data connections and defining the parameters of your strategy. You need to maximize the value of your data activities to reduce inefficiencies and bridge the gap between data engineering and science. 94% of organizations are using cloud computing, and 84% of those are using a multi-cloud data strategy. Depending on your industry, think about integrating cloud software into your data strategy to abide by security and compliance laws.
Next, establish a timeline for implementing your strategy, with milestones and proposed activities. Of course, the execution of your strategy depends almost entirely on finances.
Data engineering is crucial for any data-driven organization, but you can't expect a limitless budget in order to achieve your goals. Unlike other aspects of your business, spending more money on data engineering won't necessarily result in more financial gains.
Many experts in data science consider data engineering a "zero-sum game" — one that doesn't generate a profit but, instead, helps other people in your team do their jobs properly. This is because the right data engineering strategies facilitate almost every component of your organization, from sales to marketing to payroll. The right data at the right time — and used by the right people — expedites business operations across the board.
Still, you need to spend a decent amount of money on data engineering in order for everything to run smoothly. The biggest expense here will be hiring other data engineering experts for your team (more on that later), but even the top talent can't do much without the right software. You need to invest in programs that produce the right algorithm development, machine learning aspects, and quantitative research methods associated with data engineering.
Know how to monetize data and ascertain the value it brings to your organization. This will improve data management across the board.
If you need buy-in from investors, be prepared to prove how your data engineering strategy will benefit the organization. Use real-world examples to back up your assertions. You can use examples from competitors who are already using data to establish a competitive advantage. Invite other departments to vouch for you. The sales department, for example, might need more data to facilitate their tasks and want to participate in your strategy.
Sure, there are many off-the-shelf solutions but, if you want to play with the big boys, you need software that lets you prepare, process, and integrate data properly. If coding's not your thing, software like Xplenty lets you build your entire pipeline without any hardcore data engineering experience.
Assemble Your Team
Data engineers will work alongside data scientists to collect and validate all the information in your organization. Some data scientists are responsible for ETL, but it's a good idea to hire separate engineers to carry out these tasks.
If you want to make a great first impression in just 90 days, hiring new staff should be at the top of your to-do list. The hiring and onboarding process can take a while, so it's best to start as early as you can.
TIP: LinkedIn is a good place to start. You can attract and interact with the most talented data engineers from around the world and bypass the job posting stage.
When you hire staff, describe the changes your organization needs to make to optimize its data activities and how you plan to make these changes. There's one question you'll ask yourself early on:
"Should I outsource?"
This all depends on the scope of your organization. It's beneficial to have more data engineers than data scientists working for you because collecting clean data from disparate systems is a huge undertaking that requires a dedicated team of people. However, it's important that you choose engineers with the right programming and systems creation skills so they can support important data pipelines.
Whether you decide to outsource or hire in-house engineers, the right staff will make or break your first 90 days. Despite huge investments over the last few years, nearly half of organizations still experience difficulties bringing data and analytics into production. Good data engineers, therefore, will reflect well on you in the long run and make your first three months so much easier.
Create a Data Strategy Roadmap
Once you have created strategic goals for the next 90 days, define how you will accomplish each and every one of these goals. Create a roadmap and establish the following for each goal on that map:
- The processes you will use
- The technology you will use
- The team members who are responsible for tasks
- How much it will cost
- How long it will take
You can evaluate these goals as you progress through your 90 days.
TIP: Identify any skills gaps in your team early on so you can provide additional training to your employees or invest in new technology to help you meet your objectives.
Eliminate Data Silos
Reducing data silos will make it easier to integrate data and improve data activities across your entire organization. You should integrate data into one centralized system, which makes it easier for all departments to access the information they need. This will foster data-driven projects in your business and improve data efficiencies.
One centralized system will ensure your data is:
Describe the "whens" and "hows" of removing silos in your data engineering strategy. How long will it take? How will you do it? What tech will you use?
Know As Much About Data Engineering As Your Data Engineers Do
Working in a senior role doesn't mean you can become complacent. If you want to be a success in your first 90 days (and beyond), you need to keep up to date with the latest data engineering trends so you can make smarter, quicker decisions. This means constantly learning about the following:
This also means learning the latest about Java, Scala, Python, and all the other programming languages you and your data engineers use.
Data engineering can be a challenge —you work on data-related issues, such as mismatched data and data with missing fields. it should not mean:
- Slow uptimes
- Endless coding
- Using up all your data engineering resources
Enterprises that focus on the security of their business intelligence (BI) environment generally limit their scope to the data warehouse and the data being presented to end-users. Sensible enterprises ensure that the data in the data warehouse and any related data marts is secure, and restrict access to query and reporting tools.
If data security is a necessity in your new role, reach out to Xplenty to learn about how we use Amazon KMS to provide Field-Level Encryption for our users.
Optimize Data Collection and Sharing
Your data strategy will establish the parameters for data collection and sharing in your department. This makes it easier to collect and transfer higher-quality data for your data scientists to use. As a result, they can get more value from data.
Establish how you will optimize data collection and sharing in your data engineering strategy. This might include establishing rules for data values and naming element data, as well as thinking up best practices for accessing metadata.
Know the Law
How organizations collect, handle, and store data is in a constant state of flux. New legislation that pertains to data compliance, both from domestic and foreign lawmakers, will influence the decisions you and your data engineering make in the first 90 days.
When it comes to data collection, things can change quickly, often at a moment's notice, so don't presume things will be the same in your new job as your last one.
- Take the recent GDPR legislation, for example, which impacts organizations in the United States who collect information from customers and clients in the European Union.
- Or ever-changing HIPAA guidelines for companies that process health-related data.
You could face expensive penalties for a data breach or violation, and this isn't something you want to explain to colleagues in your first 90 days. Make sure you know the latest laws that will impact your job.
Assign Roles for Data Governance
Once you know the law, assign roles for data governance in your team. You might want to delegate the following data governance tasks to team members:
- Ensuring compliance with legislation
- Maintaining internal standards
- Keeping track of policy changes
These well-defined roles will increase accountability when it comes to data governance and protection.
Take It to the Board
Once you have established your data engineering strategy, you might need approval from company leaders. Prepare a business plan and take your strategy to managers. Leaders will help you establish the following:
- How you can improve your strategy
- The resources you need to implement your strategy, including capital investments
- The staff you need to implement your strategy
The first 90 days in your new role are an important "pulse-check" for your colleagues and investors. Follow the guide above and make sure your data engineering strategies are bullet-proof.
To kickstart your success in your new role, consider including Xplenty in your data strategy. We are a user-friendly, cloud-based ETL platform with over 100 sources, destinations, and pre-built transformations. Contact us to get started.