Data is a powerful tool in today’s business world. Used to make informed decisions, optimise processes and evaluate performance, data analysis is crucial in the information age. Large companies have the edge today – they’re able to use their vast resources and advanced analytic technologies to find patterns in huge data sets. So, what kind of insights can they gain from these large data sets, how do they gain the data in the first place, and how can small businesses compete?
Why is data useful?
Understanding what makes your business tick involves pulling together a mix of different data points. Think of it like a treasure trove of info—everything from who your clients are and your sales numbers to how your website works and the vibes you're putting out on social media.
By gaining insights from this data, you're arming yourself with a powerful toolkit for making decisions. It helps you spot trends in your business, fine-tune your operations, tweak marketing moves, make smart decisions, and handle any curveballs that come your way.
Data analytics is also great for long term strategic planning for your business. It's like looking into a crystal ball for your business. You get to plan ahead, ensuring your business keeps growing.
Types of advanced analytics
- Data mining
- Predictive analytics
- Prescriptive analytics
- Big data analytics
Data and text mining
Data mining involves the analysis of extensive datasets to reveal patterns and relationships, playing a crucial role in addressing business challenges through insightful data analysis. On the other hand, text mining focuses on converting unstructured text into organised data, facilitating seamless analysis. Both data mining and text mining contribute significantly to extracting valuable insights from diverse data sources, empowering businesses to make informed decisions and gain a competitive edge through comprehensive data exploration.
Predictive analysis refers to the process of utilising data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. This analytical approach aims to forecast trends, behaviour, or events, providing valuable insights for informed decision-making.
What should happen. Providing specialised recommendations to help make decisions, suggesting actions to achieve desired outcomes. Prescriptive analytics is the most action-oriented form of analytics, giving companies the confidence to move forward with action plans.
Big data analytics
Big data analytics involves the examination and interpretation of large and complex datasets to extract meaningful insights, patterns, and trends. This analytical process utilises advanced techniques, including statistical analysis, machine learning, and data mining, to uncover valuable information that can inform decision-making, identify correlations, and reveal hidden patterns within the vast amounts of data generated by various sources. The goal of big data analytics is to derive actionable intelligence, enhance business strategies, and gain a deeper understanding of phenomena across diverse fields, ranging from business and finance to healthcare and technology.
Companies like Google and Amazon are developing new business models that can exploit big data. And guess what? Tech giants like IBM and Hewlett-Packard are also investing in the data trend. Big data is changing the landscape. More businesses are hopping on the data-driven bandwagon, setting companies apart from the rest.
However, many companies are unsure on how to proceed with big data. They are hesitant to invest in big data because they are not sure of its effectiveness, and they are uncertain if they’ll get something better than the data they already have.
Companies need to figure out how to collect and manage data from different sources. They also need to develop analytics models that can predict and optimise outcomes. And, most importantly, management must be able to actually adapt the business to the insights the data and models provide. At the end of the day, companies need to have a clear strategy for using data to outshine the competition, and make sure they gave the right tech setup to make it all happen.
Job roles in advanced analytics
The following are some of the most common roles within data and analytics.
- Data Scientist: Analyses complex data sets and develops statistical models for business insights.
- Data Analyst: Examines data, identifies trends, and creates visualisations to support decision-making.
- BI Developer: Designs and develops BI solutions, including dashboards and reports.
- ML Engineer: Builds and implements machine learning models for predictive analytics.
- Data Engineer: Constructs and maintains data architectures for processing and analysis.
- Quantitative Analyst: Applies mathematical methods to analyse financial markets and evaluate risk.
- Operations Analyst: Optimises operational processes for efficiency and effectiveness.
- Statistical Analyst: Utilises statistical techniques to analyse data and provide strategic insights.
- Big Data Engineer: Manages large-scale data processing systems using technologies like Hadoop and Spark.
If you are interested in a role in analytics and data, or know someone who is, feel free to browse our job openings here, and get in touch!