Big data has changed organisations in a big way. The digital transformation has put data at the centre of companies of all sizes and industries in both the public and private sectors.
In this blog post, we describe what exactly big data is, why it is so important for organisations today and what technologies and strategies organisations should use to make better use of big data.
1. Introduction to the topic ‘Big Data’
Big data stands for a large amount of structured and unstructured information generated by people and machines. This data is multiplying every day — data in the order of zettabytes produced by computers, mobile devices and electronic sensors. These large and complex data sets cannot be easily managed or analysed using traditional data processing tools.
Big data comes in different formats:
- Structured data: e.g. transactions and financial documents
- Unstructured data: e.g. texts, documents, video, and multimedia files
- Semi-structured data: e.g. web server logs and streaming data from sensors
Organisations can use this data to make data-driven decisions, improve processes and policies and develop customer-centric products, services and experiences.
The need to manage large amounts of data dates back to the 1960s and 70s, when the first data centres were built and the relational database was developed. This was the starting signal for today’s big data.
Traditionally, big data has been recognised by three characteristics: Variety (Variety), Volume (Volume) and Velocity (Velocity), also known as the ‘3 Vs’. In recent years, however, two further characteristics have been added: Value and Veracity, because today data has become capital. These ‘5 V’s’ as characteristics of Big Data mean the following:
- Volume: The amount of data — for some companies this can be tens of terabytes of data, for others it can be hundreds of petabytes.
- Velocity: The speed at which data is received and (potentially) processed.
- Variety: The types of data that are available — structured data, unstructured data, semi-structured data
- Veracity: How truthful is data and how much can it be relied upon?
- Value: Data has an intrinsic value to the organisation and this value should be discovered. This value can be internal, such as operational processes that can be optimised, or external, such as suggestions for customer profiles that can maximise engagement.
2. Advantages of Big Data
- Better insights: When companies have more data, they can gain better insights for every process.
- Decision making (market intelligence): Thanks to deep data insights, companies can make data-driven decisions with more reliable forecasts and predictions.
- Improved operational efficiency: Every department can benefit from data at an operational level.
- Personalised customer experiences: Customer profiles and associated data can improve customer loyalty and experience.
- Cost savings: Using Big Data, organisations can find ways to reduce costs and improve operational efficiency.
3. How does Big Data Analytics work?
When data experts start a new project with a big data analysis, they generally follow a five-step process. We call this process the ‘data science workflow’:
- Identify business questions: The first step in turning data into insights is to define clear goals and questions: What does the business need? What kind of problem are we trying to solve? What type of data is needed? What techniques and methods will we use?
- Data collection and storage: Then the data needs to be collected from various resources such as cloud storage or mobile applications and stored in a secure location such as a data warehouse in order to analyse it.
- Process data (Processing): Once the data is collected and stored, it needs to be organised properly to get accurate results in analytical queries. Some processing options are batch processing and stream processing.
- Data cleansing (cleaning): The next step is to improve the quality of the data in order to achieve a more meaningful result. All data must be correctly formatted to eliminate duplicate or irrelevant data.
- Data analysis: Once the data is ready, advanced analytics can turn big data into understandable insight. Some of these big data analytics methods include data mining. Machine learning methods: predictive analytics, deep learning, natural language processing (NLP). Finally, the data is ready for visualisation and communication.
4. Best Big Data Analytics Tools
The most practical tools used by organisations for big data analytics projects are the following:
- Tableau: Mainly used for business intelligence and data visualisation to share, analyse and report information.
- Microsoft Power BI: It combines business analytics, data visualisation and best practices that help a company make data-driven decisions.
- Google Analytics: Is a free web analytics service from Google that provides basic analytics tools and statistics for search engine optimisation (SEO) and marketing. The performance of a website and information about its visitors are monitored and analysed.
- Python: Is one of the most popular programming languages for data science and analytics. It offers many libraries for tasks such as data manipulation, cleansing, analysis and visualisation as well as machine learning and automation.
5. Challenges with Big Data
Although big data offers many advantages for companies, there are some points that companies should consider in order to achieve better results. The biggest challenges associated with big data are as follows:
- Big data volume and complexity in architecture design
- High costs for big data projects and infrastructure
- Accessibility and slow gain of knowledge
- Skills and knowledge shortage
- Complex security and compliance
6. Conclusion
Big data plays a crucial role for companies in various industries and offers significant benefits that can increase efficiency. As big data also brings some challenges and evolves over time, we at ServiceFactum suggest some points that can increase the efficiency of big data for organisations in the future.
The following technology trends will have the biggest impact on big data:
- AI and machine learning analytics: AI and machine learning algorithms will become key to performing analyses and tasks. Automated machine learning tools will be helpful in this area.
- Improved storage with increased capacity: Storage options in the cloud are constantly improving. Data lakes and warehouses (either on-premise or in the cloud) are attractive options for storing big data.
- Emphasis on governance: Data management and regulation will become more comprehensive as the amount of data increases, requiring more effort to protect and regulate it.
- Quantum computing: Quantum computing can also accelerate big data analyses with improved processing power.