What I wish I knew about data for startups

What I wish I knew about data for startups
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What I wish I knew about data for startups

Big Data is an area that explores various methods to examine and regularly remove large amounts of data, says proglib.io. It includes the use of mechanical or algorithmic processes for obtaining operational data to resolve difficult business dilemmas. Specialists in data science agencies work with unstructured data, the analysis results of which are used to support decision-making in business.

One of the definitions is: “it can be called large when its size becomes part of the problem.” Such volumes cannot be saved and processed using the traditional computational approach for a given period. But how big does the data have to be called big? We regularly talk about gigabytes, terabytes, petabytes, exabytes, or more general units. This is where the misconception arises. Even small data can be called large depending on the context in which it is used.

For example, a mail server may not allow sending an email with a 100-megabyte attachment, or let’s say we have about 10 terabytes of graphic files that need to be processed. Using a desktop computer, we will not be able to complete this task within a given period due to a lack of computing resources.

How is big data classified?

Let’s distinguish three categories:

  • Structured data that has an associated table and relationship structure. For instance, stored in a DBMS, CSV files, or Excel spreadsheets.
  • Semi-structured does not follow the strict structure of tables and relationships but has different markers to separate semantic elements and provide a hierarchical structure for records and fields. For example, information in emails and log files.
  • Unstructured data does not have any structure associated with it at all or is not organized in a defined order. Typically these are natural language text, image files, audio files, and video files.

Where Big Data is Managed

Where Big Data Is Managed

Analytics are applied in a wide variety of areas. Let’s list some of them:

  • Healthcare providers need analytics to track and optimize patient flow, track equipment, and drug usage, organize patients, and more.
  • Travel companies are applying analytics techniques to optimize the shopping experience across multiple channels. They further analyze consumer preferences and desires and find the correlation between current sales and follow-up views, which allows them to optimize conversions.
  • The gaming industry uses it to get information about things like likes, dislikes, user attitudes, etc.

Features of Big Data

It is characterized by four rules (Volume, Velocity, Variety, Veracity):

  • Volume: Companies may collect a large amount of information, the measurement of which becomes a critical factor in analytics.
  • The rate at which data is generated. Almost everything that happens around us (search queries, social networks, etc.) produces new data, many of which can be used in marketing decisions.
  • Variety: The information created is heterogeneous and can be presented in a variety of formats such as video, text, tables, number sequences, sensor readings, etc. Understanding the type is key to unlocking its significance.
  • Credibility: Credibility relates to the quality of the analyzed data. They contain, with a high degree of confidence, many records that are valuable for analysis and that contribute meaningfully to the overall results. On the other hand, low confidence data contains a high percentage of meaningless information called noise.

Here are some further tips to take into Account

A good analytics team is an investment

Analyzing data can be complicated. Excel spreadsheet enthusiasts recently mastered this skill, but now it’s a specialized skill for data analysts and scientists. Excel has its place, but if you can hire a team with more advanced skills, you’ll be able to leverage your data more effectively.

In addition to spreadsheet skills, a good data analytics team will have programming skills in Python or R, SQL expertise, and a good understanding of statistics.

Your startup needs to know what is realistic. You should hire data analysts with ambitious but reasonable expectations and find those who can grow with the company as it grows.

Obtain the Right Data

Your data analytics relies on data. It won’t matter how skilled your analysts are if you don’t have good data to work with. Provide your analysts with accurate and meaningful data. You can ask your analysts for advice if you’re not sure what’s needed – if you’ve hired well, they’ll know what’s needed and what isn’t.

When you have an eCommerce startup, you’ll likely already set up Google Analytics or another analytics tool. However, you may also want an A/B testing extension or package to investigate how different copy or page layouts affect user experience.

Is a heatmap necessary for an e-commerce company to understand click patterns? Not necessarily. This type of information would be ideal for a startup building a mobile game to make the product better by making specific interface choices informed by players’ actions.

Depending on your business, you’ll need different things. Your analysts can perform better analysis when you have more good data, which you should research and implement as early as possible.

Early technology decision-making is critical

Choosing your tech stack early is essential as well. Having a bad foundation won’t allow a company to thrive, and constantly switching out the solutions in your stack will ruin your data analytics. You will be able to perform a range of analyses based on the choices you make today, including those related to databases and firewalls.

It can be crippling to choose the wrong infrastructure. In recent years, NoSQL databases such as MongoDB have become increasingly popular because they enable rapid scaling and development. The downside is that you can’t join data types across — traditional SQL databases like MySQL are much better at this than CouchDB. While some of those features might not seem necessary during the early stages of the project (and maybe you won’t need them), keep in mind that going back to make changes once your system is up and running will be disruptive and costly. In the beginning, it’s better to choose a tech stack and a database solution that your team can grow into so that your analyses won’t be limited.

Thank you for reading!

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