data united. 1-0

Can Data have Teamwork?

TL;DR

How do you consolidate data from many different sources in your company? How many of your datasets are now independent of each other? How long does your team take to consolidate them just to make an outdated reports for you?

Let’s have a look at what are the challenges in managing data. Then, we will see how data can be gathered from different sources. When our data is working as a team, we would gain more knowledge about our business.

United Challenges

In all simplicity, most companies get data from softwares they have implemented (we hope hand-written pen and paper data is not our option). The software could be ERP, CRM, SCM, HRIS, or others. Users interact with the software and the software stores data. That is it.

Your company may start with sales department. It implements sales software and you have sales report. Later on, you have warehouse. It implements inventory software and you have inventory report. Then, your marketing department implements CRM software. Then, your accounting team implements accounting software. And the list continues.

Each implementation must be integrated. For your business to be efficient, flow of data must be seamless within and across internal departments. Why? Because as top management, we want helicopter view of our company derived from all aspects of each business process.

Many implementation of different softwares would make such report hard to produce. At the least, it takes few days and extra effort from each department to lay out a 4-day-late data in an ordinary graphical representation.

Been there, done that. But we have solution for such situation. We have implemented it successfully for our client, maybe it is now your turn.

Get United

Now it is time to bring your company to the next step. First, we identify sources of data. As mentioned, it could come from different software. And, it also could come from unstructured form of data registration, such as employee notes, verbal communication, or chat platform. Identify source of data, prioritise them, and define which source is taken into account.

Second, execute data extraction. Depending on the source, many different techniques are implemented to extract, transform, load (ETL) data into a unison format. For example, if your data is in spreadsheet, regular ETL process must be triggerred to dump its data to a more structured database. Another example, if your data is in databases, SQL can be used and scheduled to load everything into a centralised database.

Lastly, we code and automate the ETL process as much as possible. It reduces human error, save time, and very scalable to manage huge data streams.

Data is like football team. Individually, it means nothing. Having them compliment each other, brings considerable information and knowledge for our company and basis on business decision.

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