Data is the new oil, they say. These words can be no truer. For businesses in the 21st century, data is vital. Data enables decision-making, powers marketing, predicts users’ needs, etc. Almost all aspects of running a modern business require data. This business reality makes properly ingested data more important than ever.
The current business landscape is defined by fierce competition. Owing to this, processing, analysing, and interpreting data faster can be a competitive advantage. Now, when data is decentralised, it becomes more complex to maximise them in real-time. This births the need for data ingestion.
Breaking Down Ingested Data
In the current world, businesses get data from various sources. For example, data sources can range between website databases, social media, surveys, SaaS data, Web scraps, etc. Data Ingestion is, therefore, the process of bringing data from disparate sources to a single location through specialised tools.
The destination for data from the various sources can be a database, document sheet, data warehouse, data mart, etc.The process of creating ingested data is to facilitate easy analysis and leverage. However, you may not need to ingest data enterprise-wide.
Some departments may require that you keep all their data separate. For instance, sales data can be ingested from several sources and stored in Salesforce. Product data can be stored somewhere else. The list goes on. The whole idea is to make data easy to analyse when need be.
Importance of Data Ingestion
Data can make or break a company. Startups and corporations invest heavily in the data analytics team for the benefits it holds. Proper ingested data can result in the following for a business:
Ease Analysis
Businesses rely on analysis for decision-making, future projection, understanding the audience, etc. So it’ll be counter-productive if a business only tries to sort through its data when it’s in dire need of it.
The pool of data available increases by the day. A large amount of data is being generated from multiple sources. With data ingestion as the primary stage of data analysis, making sense of data becomes easier.
Ingested data has been cleaned, sorted, and altered in ways that make it easily understood in the future. The company can then leverage the single pool of data to beat the competition, build a better product, create the right advert and marketing strategy, etc.
Data Enrichment
‘What is data enrichment?’ is one of the many questions analysts have to answer to product, marketing, or sales experts. In layman’s terms, data enrichment involves making a base of data richer, introducing more content and context.
The data enrichment process involves merging third-party data to internal first-party consumer data to attain better insight and understanding of the subject. The data enhancement process is facilitated by data ingestion, which brings data from other sources to improve the current data.
Maximise and Scale Your Ingested Data Ops
Getting ingested data can be laden with problems. Consequently, your business must make deliberate efforts to mitigate them. Here are ways in which your business can maximise data ingestion:
Identify the best process for your business
To maximise data ingestion, you must identify the best process for your company. There are different types of data ingestion processes. Each of the data ingestion methods comes with its respective pros and cons. Hence, you must understand what commitments and trade-offs you are making with each type.
Firstly, there is the real-time data ingestion process. Real-time data ingestion is also known as streaming data. Streaming data is best used when the data you collect gets obsolete very fast. Hence, your business needs a constant update. The streaming data ingestion method collects, processes, and stores data upon generation.
Secondly, there is the batch data ingestion method. As the name suggests, the data collection process occurs at intervals. The intervals, in this case, are scheduled and recurrent. Therefore, batch data works best for your business if you have repeatable processes, e.g. reports.
Lastly, Lambda architecture seems to bridge the gap between the two methods mentioned above. Lambda architecture combines streaming and batch data frameworks. However, Lambda architecture certainly costs more.
Always pre-empt difficulties
Irrespective of the data ingestion process you use, there’ll always be difficulties. Most importantly, the job becomes more complicated as data volume increases. Hence, it’s important to have a system to monitor, preempt and proffer solutions to complexities before they arise.
Preempting difficulties begins with properly understanding the challenges that come with the method you choose to get ingested data. If your current data ingestion system has no in-house expert to cater to it, it’s best to hire outside help.
Automate the process
Data is bound to increase in volume and complexity by the hour. Leveraging manual techniques to cater to the data won’t be productive. Hence, set up automation as much as you can. Several data ingestion tools can help with automation; leverage them as much as you can—the results from automation include; safety, architectural consistency, error management, and consolidated management. Ultimately, you save time.
Summary
When you handle ingested data properly, it can push your business to the next growth phase. Hence, it’s important to put everything that maximises the process’ potential in place. The overhead cost, in the long run, is always worth it.
