“It is a capital mistake to theorize before one has data.”
Sherlock Holmes in “A Study in Scarlet”
From a plethora of complex data available on a daily basis, businesses must find the most valuable information. Every company can gain better insights from the data it gathers with data science services. IBM experts in Analytics: The Real-World Use of Big Data found that 71% of banking and financial markets firms report that using data and analytics creates a competitive advantage for their organizations. Data-driven decision-making is now a must for every company.
The power of the suitable data set
“In this world of big data, basic data literacy – the ability to analyze, interpret, and even question data – is an increasingly valuable skill,” says Harvard Business School Professor Janice Hammond. Data science is the process of creating, scouring and structuring datasets to analyze and extract crucial information. As Harvard Business School Guide to Data & Analytics puts it, data science in business collects, organizes and maintains data, often by writing algorithms that make large-scale analysis possible. Humans miss information or trends that algorithms can detect.
According to a Harvard Business School, companies can use Data Science to:
- Gain customer insights – learning a lot about your customers’ habits, demographics, preferences and aspirations is possible by looking at their data. Correctly applying Data Science can lead to identifying and analyzing customer behavior patterns, improving user experience and mitigating potentially negative customer decisions like cancellation of service.
- Increase security – overcoming cybersecurity threats is one of the biggest challenges companies face. Machine-learning (ML) algorithms, combined with Data Science, can significantly assist in conducting security audits, predicting potential threats, detecting system intrusions and helping developers and engineers ensure your customers’ safety and secure your business.
- Inform internal finances – unlocking data’s potential can benefit organizations by generating reports and forecasts considering potential profits and losses, assessing financial risks and proposing alternative solutions. Forward-looking financial services and banks seem ideally suited to take advantage of such solutions.
- Streamline manufacturing – optimizing production performance and detecting deviations or undesirable patterns cannot be done manually with such a high volume of presently collected data. Data-driven algorithms detect failures and gather insights to prevent costly and unexpected challenges by finding and eliminating their possible sources.
- Predict future market trends – collecting, processing and analyzing data enables you to identify emerging trends in your business sector. Data Science will directly help your company improve its decision-making process and help you grow faster than the competition.
Unlocking the full potential of your company with data will only be possible with the help of a data scientist. According to the US Bureau of Labor Statistics, data scientists’ employment will rise 15 percent by 2029 – faster than the four percent average for all occupations.
NLP-based solutions for high performers
Generative AI and large language models have become the “talk of the town” in recent months, but data science services have been incorporating natural language processing (NLP) and the capabilities of artificial intelligence (AI) to empower companies for a long time. Data science has seen significant advancement in the past few years with the introduction of AI-powered NLP and natural language understanding (NLU) techniques. These technologies have revolutionized how data scientists work with unstructured text data, making extracting valuable information from it easier. Using NLP, data scientists can perform sentiment analysis, entity recognition, topic modeling and document classification, among other things.
One of the most significant benefits of NLP is its ability to enhance data science services in areas such as customer feedback analysis, scientific analytics and content analysis. With the help of AI, data scientists can now analyze vast amounts of text data quickly and accurately, providing previously impossible insights and revolutionizing how businesses understand their customers and make data-driven decisions.
Integrating AI-powered NLP in data science has opened up new possibilities for businesses and organizations. It has made extracting valuable insights from unstructured text data more accessible, which was previously a difficult and time-consuming task. According to McKinsley’s The State of AI report, companies able to implement AI-related solutions in the field like data science will be able to pull ahead of the competition and become high performers. Not doing it might result in lowering efficiency and creating a less attractive environment for talent.
Data gives you the needed edge
In 2006 Clive Humby, mathematician and marketeer created, coined the term “Data is the new oil.” According to Statista, humans taking advantage of various technologies will create, capture, copy and consume around 120 zettabytes of data. It comes as no surprise that with the ever-growing importance of AI, by 2025, this number is projected to grow to more than 180 zettabytes. One zettabyte is approximately equal to 1 billion terabytes.
Numerous organizations are unaware of the potential value of their data and are missing out on opportunities. Failing to collect, analyze and organize data can lead to missed insights and a competitive disadvantage. It’s essential to recognize the importance of data science and AI and take action to stay competitive in the market. Don’t fall behind – start utilizing your data today. If you want to gain better insights from the data your company gathers, contact our experts and gain a competitive edge.
About the authorMarcin Sieprawski
Head of BIg Data Lab
An experienced system architect and R&D project manager with 20+ years of enterprise software design and development expertise, Marcin is the founder and leader of the Big Data Lab at Software Mind, which focuses on R&D, Data Science, data-driven innovation and high-quality agile software development. Along with developing Big Data solutions before they became mainstream, he has participated in and led commercial projects connected to Big Data and high volume and high-velocity solutions, throughout various sectors that have supported telco operators, international telco interoperability hubs, banks and financial institutions and content delivery network providers. He has experience in all project lifecycle phases, including requirements gathering and analysis, architectural analysis and design, data modeling, implementation, deployment, coordinating and mentoring.