How Time Series Data Management Can Help You Scale Your Business

Did you know that time series data has been hailed as the ‘data of the hour’ by many data analytics experts?

Often, organizations use a database management system (DBMS) to harness the value of time series data in conjunction with other useful business information.

It is widely used in many various problem areas and industries and has effectively helped businesses both big and small.

But how exactly can entrepreneurs utilize time series data management to scale their businesses? Keep reading to find out.

What Is Time Series Data Management?

So what is time series database management? MongoDB explains that a time series database (TSDB) is an optimized system responsible for storing and querying data that has the tendency to change over time.

This data, also called “time-stamped data, ” is gathered from a single resource to observe progress and ensure consistency.

Data that changes by the second, minute, day, month, year, and custom time intervals can all fall under the time series data category.

Some examples include gas prices, utility consumption, and company growth.

What Are Some Time Series Data Management Methods?

There are five popular time series methods that are geared toward business development. 

Time Series Regression bases future predictions on previous response history.

You can often see this implemented in stock prices, energy consumption, and temperature readings.

This type of data management is meant to estimate how independent variables impact a dependent variable. 

Time Series Analysis in Python was developed mainly for the financial sector, but also sees usage in media and retail.

Python inherently has libraries that data scientists can use to monitor the time series using various models.

NumPy, pandas, and statsmodels are just some of the packages used here.   

Time Series in Relation to R involves the programming language R, which it uses a collection of dedicated packages to model and analyze data over determined intervals.

Preprocessing and decomposition are often used here to see seasonal, residual, and trend results. 

Finally, Time Series Data Analysis is performed by collecting data at different points in time using statistics, visualization, testing, and evaluation.

Deep Learning for Time Series works with long sequences, particularly because of the ability of deep neural networks to learn complex patterns and mappings.

How Can You Scale Your Business Using Time Series Data?

These different methods are used, among other things, to forecast business metrics such as stock market price, sales, and turnover, and in turn, develop business strategies.

Furthermore, AI can be trained and leveraged to extract features on their own from raw input data, which will make scaling procedures much easier.

Businesses in manufacturing, for instance, can use time-stamped failures to identify production root causes, or time-series data from physical sensors to improve day-to-day operational decision-making. 

Besides the manufacturing industry, the logistics industry also makes good use of time series data to coordinate transport, labor, and environment.

The smart devices industry, stock market, and trading industry also commonly use time series data in their business concerns.

Market investors and traders use time series data analysis to see whether a company is doing better or worse depending on the measures they care about. 

In general, businesses from various industries can benefit from time series data management because it can reveal more efficient methods and solutions for scalability.

For instance, analyzing frameworks can help companies handle demanding workloads and growing volumes of data.

Using predictive models, businesses can make use of deep learning to anticipate potential challenges and necessary resource allocation.

Analytical data over set periods of time can also help with performance monitoring and decision-making.

Because this model allows data to be managed and analyzed in real-time, businesses can be more informed when expanding or maintaining operations.

What Is The Outlook For Time Series Data Management?

The time series data management market was worth over $273.53 million in 2020 and is projected to grow up to $575.03 million by 2028, growing at a compound annual growth rate (CAGR) of 10.06%.

InfluxData, a specialist in time series data management, also recently raised $81 million and introduced a new database engine called IOx. 

Businesses are also looking to invest in more tech to accommodate large amounts of time series data efficiently and effectively, which is why there have been specialized databases growing in popularity.

Experts are expecting time series data management to continue playing a crucial role in various industries, particularly as we see the rapid growth in energy, healthcare, finances, logistics, and IoT integration. 

The Future Of Time Series Data Management

Overall, time series data management is crucial for businesses to best harness the power of data for their growth.

There are different methods for time series data management, so it’s best to select one according to your business structure and resources. 

In the future, businesses and organizations that fall under fields like economics, social sciences, medicine, epidemiology, and even governance are expected to adopt this technology to help them find and develop controllable solutions.

For more articles on tech and business, feel free to check out the rest of our blog here at Quantum Marketer.

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