Connect Data from Linkedin to Sisense

You can get your LinkedIn data into Sisense to make more informed business decisions. This simple and quick guide gives you easy instructions on how you can extract your data from LinkedIn and then analyze it in Sisense. Our LinkedIn to Sisense connector allows you to access your LinkedIn data so you can easily manage various media objects, view metadata, and comments, and get reliable insights and metrics, such as reach, impressions, follower-like ratio, and likes. Integrate your LinkedIn with Sisense to turn your LinkedIn data into actionable insights.

LinkedIn is an extremely popular social media network that is focused on professional relationships, employment opportunities, and business listings. LinkedIn is a great employment-oriented and corporate social networking platform that is primarily used for professional communication and networking. With this platform, employers and job seekers can easily create profiles and form strong and lasting professional connections. The website offers recruiters and marketers the ability to advertise on its platform via paid social posts and in-mail. With over 610 million members worldwide, which include executives from most Fortune 500 companies, LinkedIn is easily the world's largest and most used professional network on the Internet.

Sisense is an amazing business intelligence platform which lets you join, picture out and analyze information you need to make more intelligent and better business decisions. With Sisense, you may easily unify all your Facebook Ads data into visually appealing dashboards through a user-friendly drag and drop interface. Its intuitive business intelligence reporting component is an important element of a software stack which also includes incredible tools to prep, connect, and govern data use.

Extracting data

Before loading your data into Sisense, you will have to prep it first. If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. LinkedIn's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Preparing data

Note that LinkedIn makes its data available to its users through its REST API. This API offers data on several things, such as ad insights, estimated positions, estimated bids, and many other types of data.

Loading data

Sisense is great as it enables quick and easy access to tables within your CSV files. You can upload data in two ways. The first option is to upload your data file to the Sisense Server. As soon as the file is uploaded, your data would be imported into the web-based ElastiCube as it was at the time you uploaded the file. The other option is to define your file location on the Sisense Server. You can import your Instagram data from the CSV files by following these steps: Create a new ElastiCube in the Data page of the ElastiCube Manager. Click in the ElastiCube.  You will see the Add Data dialog box. To open the CSV settings, click CSV. Click on File Upload. To upload your data file, click Browse, and then navigate to the file that you need to upload. Now select the CSV files you want to upload and click on Next.  You will see the Select Table list. You have to click on to select preview the relevant columns and then display the Settings. These settings provide more options to customize your data. You can define the following settings in the Settings area:• Culture: This defines various settings, like the format of your date.• First Row has Field Names: This enables you to easily specify column names in the table depending on the first-row header of the spreadsheet.• Delimiter: This allows you to select the character which separates different values within your CSV file. After selecting all the required tables, click Done. Your tables will be added to the schema in Sisense.

Keeping data up to date

If you've made it this far, congrats! You probably have a written a program or script to extract your data and move it into Sisense. Now it's time to think about how you will keep this data up-to-date by loading updated or new data. Of course, you can just replicate all your data every time you have updated your records, but that would be extremely manual and time-consuming. Luckily there is a better way. The key is building your script so that it can sense incremental updates made to the data. Thankfully, LinkedIn’s REST API results include fields so that you can identify those records which are new since the last update you made (or since the most recent record you have copied). Once you have taken new data into consideration, you can easily set your script either as a continuous loop or cron job to pull down new data as soon as it appears.

The Easiest And Fastest Way To Do It

If all this sounds a bit overwhelming, don't worry -- there is an easier way to get this done! Thankfully, products like Improvado were developed to move data from LinkedIn to Sisense automatically. Using Improvado, you can easily combine the most crucial and relevant data from your ad campaign into a dashboard. You can then connect this data to Sisense.