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Who doesn’t love a good C word? Chocolate! Candy! Champagne! But when it comes to your data, those three favorites of mine aren’t the main ones you need. During this seven part series focused on talent data, we’re going to discuss the 7 important C words that drive best practice data management. They will all matter to you and your outcomes, but this first one is VITAL!
That vital first word is Connected. Your talent data MUST be connected.
You have talent data in a lot of places - your HRMS/HRIS, LMS, ATS, CRM - and how many more? Don't forget all the juicy data in the public domain like publications, blog posts, and skills, which could be anything from education to experience or hobbies! When your data stands alone in silos, you can bet you’re missing the power of having it connected and in one place, where you could analyze, search, access and share it any way you need to.
So, let's get this lot connected - and let's do it the new school way - with algorithms, and in a data lake. Stop thinking of the burden of Extract, Transform, and Load (ETL) for a predefined, limited warehouse. Stop letting single point to point integrations create a spider web of system connections. Instead, start letting tech do the grunt work and get it ALL under control.
Takeaway: Instead of ETL's to load data into a warehouse, use algorithms and live "data connectors.”
Connectors are what we call sets of algorithms that gather data sets, organize the data and allow them to be connected to other data sets.
But why use algorithms to connect your datasets instead of the traditional ETL? Well, there are several reasons:
1. Algorithms don't need unique identifiers to connect records
This is a BIG deal because you are unlikely to find the same identifier across a wide range of data sources. In earlier warehouse models, we only connected record sets where they had the same value (like an employee number or email address). But think about internet data and blog posts - there's no unique identifier there. Until now, that meant these records were VERY difficult to accurately connect. No more.
We at SwoopTalent, the Talent Data Platform from Talent Data Experts, call ours correlation algorithms. These algorithms look for common data points across entire rich records. They don't depend on a match in a single field, so you can connect a lot of datasets you once thought you couldn't include. THAT leads to richer analytics and faster decision making.
2. Connectors can be in real time
You might want “apply status” pushed hourly from your ATS to your CRM so you can adapt to every step in the cycle. If your employees start adding “new skills” on their public profiles then the data can be pushed straight to your LMS or other HR technologies when they appear on the internet to track the impact of programs. How about alerting the manager when an employee blogs on a skill you're looking outside for?
You can't do any of these if you don't connect the data. Plus you can't do them fast enough if you're waiting for an ETL.
3. Connectors are "set and forget”
You might have an ATS connector, a CRM connector, and seven others for the best of breed apps you are using. Plus you have one for spreadsheets, six for business systems, and one that ingests resumes. They are all set and forget. Every one of them, bringing your data together 24x7 without you thinking about it.
4. Connectors are hub and spoke, not point to point
Traditionally, most talent systems are connected point to point, like a spiderweb, which means:
These are not good. However, if you replace your current spiderweb with a hub and spoke, data lake based approach, they both disappear. You can then plug and play anything and never lose the data.
Takeaway: Modern data connectors enable plug and play integrations as well as data connectedness
Connecting and Collecting for a Talent Data Lake
A data lake is a centralized repository that allows you to store all of your structured and unstructured data at any scale.
How is a data warehouse different from a data lake?
A data lake is not the same as a data warehouse. Many companies are using data warehousing to analyze relational data coming from transactional systems and line of business applications.
The data structure and schema are defined in advance to optimize for fast SQL queries, and the results are typically used for operational reporting and analysis. Data is cleaned, enriched, and transformed so it can act as the “single source of truth” that users can trust. The downside is that a lot of rich, important data simply does not fit that paradigm.
A data lake is critical to your strategic goals as it assembles all of your Talent Data for use with your current and long-term talent requirements. A Data Lake is different than a warehouse, in that it stores relational data from line of business applications, and non-relational data from mobile apps, IoT devices, and social media. The structure of the data or schema is not defined which means you can store all of your data without careful design or the need to know what questions you might need answers for in the future. Different types of analytics on your data like SQL queries, big data analytics, full-text search, real-time analytics, and machine learning can be used to uncover insights.
In other words, data lakes enable your data scientists, data miners, and data analysts to do a range of activities including data movement and run various data processing and analytics, such as machine learning and real-time analytics.
BUT - your data lake can also feed a data warehouse, and that too can be automatic! Best of both worlds.
When properly executed, the data lake can become a hub of information and streamline all things HR related. This information can help you make more informed decisions and help develop the strongest team and strategy for the task at hand - and a thousand other uses you haven't even thought of yet.
Takeaway: Data lakes automatically feed your data warehouse, but they solve many other challenges, too
Connected data means that you’re putting data at the forefront of your people operations and putting it to work for your company. As a business becomes increasingly dependent on data, companies able to harness technology to connect data have a distinct advantage over their competition.
Since being connected is only one part of the 7 C’s of Talent Data, the next step is also critical. Be sure all of your connected data is Current. Is it stale and dead, or is your talent data kept refreshed and alive? Part 2 of our series will review the important C word: Current!
At the end of the series we'll release a full paper of the 7 key C words in talent data, including a few BONUS case studies! Sign up here and we'll email it to you as soon as it is ready!