Uncover hidden patterns and key insights in data your business gathers. Use data science to grow your enterprise and make better decisions by analyzing vast volumes of data.
Data science is the art of understanding patterns and relationships by employing algorithms, processes, scientific methods and systems into studying data. From the composition of analytics, statistics, modelling and computer science, emerge answers to the most pressing questions of business. In times of growing volumes and types of data, overlapping with an increasingly competitive economy, technology becomes a priceless ally. Data science services are no longer restricted to just statisticians and mathematicians but shapes the modern world by serving a great role in forecasting, reducing risk, detecting fraud and improving operations efficiency.
Data science equips enterprises with techniques and tools capable of translating business data into powerful insights. Results of those are capable of transforming industries in line with current needs and trends.
Combining a range of statistical techniques makes predictive analytics an answer to the challenges of various industries. Resulting in applications such as Pattern Recognition, Data Classification and Data Clustering, analytics are of help to e.g.customer segmentation in recommendation systems (streaming services), detecting patterns in e-commerce customer behaviour (online marketplaces), dynamic pricing based on shopping patterns and popularity (plane tickets, hotels), logistics optimization (food delivery services).
The process of empowering your business with data science starts with the identification of your data sources, followed by its evaluation, recognition and modelling. As a result, data models provide answers to which processes can be streamlined and what solutions applied. Understanding the data you possess leads to better decision-making, improving processes and designing more efficient, market-ready solutions. Consulting services let you turn models into commercially viable solutions and integrate various systems by incorporating data science into your enterprise.
By analysing patterns of transactional data and identifying links among occurring events, enterprises can define risks and opportunities. Interpretation of historical and current data provides a predictive score to make predictions about possible future events. Analytics lead to improving customer retention, boosting cross-selling and reducing risk due to generating credit scores.
Engaging the power of data through AI and NLP creates new opportunities and analysis of values hidden in the text is one of them. Discovering and extracting information from social media activity, website chats, business emails, feedback forms and surveys results in a better understanding of textual data. Text analytics is capable of sentiment analysis, text extraction, topic analysis, clustering and many more, leading to, among others, improving customer experience and online brand monitoring.
Though this be madness, yet there is a method in ‘t, citing the Bard of Avon. Data in its chaotic, raw form may cause troubles, as it is confusing, disordered and seemingly useless. But as soon as the right tools are applied and adequate processes employed, an orderly image appears. The value of your data depends on how well it is managed and how it can cater to the business needs. By analysing patterns of transactional data and identifying links among occurring events, enterprises can define risks and opportunities.
This model is a chance for enterprises lacking data analysts or data scientists. Providing services in the widely understood study of data helps in concluding structured and unstructured data. Cloud-based platforms of external providers aggregate your data and dedicated teams of analysts curate your datasets. With improved data governance and ease of deployment, DSaaS offers value to modern enterprises.
Data science enters all industries and retail is no exception. Predictive analytics improves customer experience through an understanding of consumers’ behaviour and market insights. The analysis of customer preferences is used by global brands, such as Amazon and YouTube. Recommender systems improve the understanding of needs and wants. As a result, brands create tailor-made offers and campaigns based on behavioural analysis, improve product placement based on heat sensor analysis and streamline logistics through efficient transportation and inventory management.
Finding the right talents for your organization may be a tedious task. With the support of data analysis, talent acquisition becomes easier and more efficient. Data evaluation streamlines combing through endless job applications and candidate databases. Comparing and assessment of potential candidates’ information is possible through data science solutions and leads to more efficient selection and recruitment processes, scalable to your business needs.
Making strategic decisions based on structured data lowers the risk of misguided steps. Analysis of statistics and hard data enables building predictive models, simulating possible outcomes of certain actions. Supporting the decision-making process with data science can help choose the right steps to achieve the best results while mitigating the risk.
Definition of the target audience
Gathering data with e.g. Google Analytics is an inseparable part of online business presence, but just collecting pieces of information does not work wonders on its own. Engaging data science algorithms for advanced analytics gives insights into key target group metrics and identifies the right demographics. Companies benefit from statistical observation and other tools, learning more about their clients and, as a result, provide better-adjusted products and services.
The development process in our data science consulting firm consists of three stages that let our clients minimize the risk and costs of their projects.
We define your challenge, conduct a workshop session and propose an initial solution.
We propose a complete, long-term solution and plan.
We divide your project into smaller pieces that can be achieved within 1-2 sprints – and develop the first one.