wHAT bUSINESS problems can be solved with Data?

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Custom Web Scraping / Data Collection : Pull data from numerous websites and repositories.

Data Cleaning and Preprocessing: Ensuring data quality by removing errors, handling missing values, and transforming data into a usable format.

Exploratory Data Analysis (EDA): Analyzing and visualizing data to understand its characteristics, distributions, and relationships.

Statistical Analysis: Applying statistical methods to gain insights from data and test hypotheses.

Machine Learning: Developing and applying algorithms that allow computers to learn patterns from data and make predictions or decisions without explicit programming.

Feature Engineering: Selecting or transforming relevant variables (features) to improve the performance of machine learning models.

Data Visualization: Creating visual representations of data to effectively communicate insights and trends.

Model Training , Evaluation, and Deployment: Building and training machine learning models, and evaluating their performance on new data.

Interpretation and Communication: Translating technical findings into actionable insights for non-technical stakeholders.

Domain Expertise: Understanding the specific context and industry to ensure that analyses and models are relevant and effective.

Data science has applications in a wide range of fields, including business, healthcare, finance, marketing, social sciences, and more. It plays a crucial role in enabling organizations to leverage data-driven decision-making, predictive analytics, and automation. As technology and data continue to grow, data science is becoming increasingly important for organizations seeking to gain a competitive edge and drive innovation.

Speed is a weapon. Superior speed allows business to seize the initiative and force the competition to react to you.

We facilitate Business Intelligence with an array of actuarial software suits to give you the most accurate KPIs.

Absolute analytics beats ai absolutely

Products

Product: Feasibility Study

Feasibility Study – A scientific research product, prompted by one of two things (preferably both): business question(s) and / or data. Each business question is interrogated along with the data. Generally reducing the business question(s) into one type of question: “What do you want to classify or predict?”.  The question is explored starting from the Extract, Transform, and Load phases. Follow on questions such as the data volume, velocity, and End-User purpose are discussed. If the data is provided, then the data will be mined and findings disseminated back in a report for the client. The answer to the business problem(s) may be found in the data or the data may reveal and answer to a bigger industry question(s). Sometimes the data is just random unworkable noise and no human or machine can predict or classify random noise. If possible, during data exploration, certain features may be engineered for additional discovery. When exploring the data, the visualized probability distribution of the Independent and Dependent variables will dictate if a machine learning model(s) can be used. If the variables fit the assumptions of the models, then we explore those models. Initially exploring with unsupervised machine learning, to uncover unseen patterns. Then we isolate Independent Variables attempting to predict or classify using a series of machine learning models. If we can use supervised models (labeled data), we conduct the model training, testing, scoring, and initial optimization. The model scores will be presented in the Feasibility Report, along with all the visualizations, and objective findings in the data. Report also contains a road-map for readiness of the model deployment. It may contain instructions for data pipeline development.

Another way to explain the Feasibility Study, is the world of physical mining for earth elements. Imagine that the BloomingBiz Marketing Co. is also a physical mining company that mines for ore underground. Clients employ us to mine for elements on the Parodic Table. We can also make robots that can classify or predict the earth elements in the raw ore. Clients come to us with the ore from their land; We inspect the ore, clean / refine, and generate a report of all the elements in the ore. Then we see if we can make an accurate custom robot for the end-user to interact with the ore elements. Sometimes clients send us core samples of quartz, looking specifically for gold. If there is gold, we can find it in the quartz with particular-robots. We can extract, process, and test the gold for purity; We can setup these robots on assembly lines of crushed quartz. We construct and periodically inspect the entire assembly-line for quality assurance. Given the linty of forms to fill-out, The Feasibility Study gives the clients a full-roadmap of how likely this process to work for them as well as a manual for the proper care and operation of the assembly line. The report of the findings will be presented to the client.

Data Cleaning and Preprocessing Services: Offering data cleaning and preprocessing solutions to help clients organize, clean, and prepare their raw data for analysis. This can include tasks such as data deduplication, missing value imputation, outlier detection, and standardization.

Insight Generation: Big data feasibility studies enable clients to unlock valuable insights from their vast and diverse datasets. By analyzing large volumes of data from various sources, clients can gain deep insights into customer behavior, market trends, operational efficiency, and other critical aspects of their business.

Data-Driven Decision Making: With insights derived from big data analytics, clients can make more informed and data-driven decisions. By leveraging advanced analytics techniques, such as predictive modeling and machine learning, clients can anticipate market changes, identify emerging opportunities, mitigate risks, and optimize business processes.

Competitive Advantage: Big data analytics can provide clients with a competitive edge in their industry. By harnessing the power of data, clients can identify unique market opportunities, differentiate their products or services, enhance customer satisfaction, and stay ahead of competitors.

Cost Reduction: Big data analytics can help clients optimize their operations and reduce costs. By identifying inefficiencies, streamlining processes, and optimizing resource allocation based on data-driven insights, clients can achieve significant cost savings and improve overall profitability.

Innovation and Product Development: Big data analytics can fuel innovation and drive product development. By analyzing customer feedback, market trends, and performance metrics, clients can identify unmet customer needs, develop innovative products or services, and enhance existing offerings to better meet customer demands.

Risk Management: Big data analytics can help clients identify and mitigate risks more effectively. By analyzing historical data and detecting patterns or anomalies, clients can anticipate potential risks, such as fraud, cybersecurity threats, or supply chain disruptions, and implement proactive measures to mitigate them.

Scalability and Flexibility: Big data technologies offer scalability and flexibility to handle large and diverse datasets. Clients can scale their analytics infrastructure as their data volume grows and adapt their analytics strategies to evolving business needs and market dynamics.

Compliance and Regulatory Requirements: Big data analytics can help clients ensure compliance with regulatory requirements and industry standards. By analyzing data to identify compliance issues, monitor regulatory changes, and implement controls, clients can reduce compliance-related risks and avoid penalties.

The Feasibility Study may indicate if additional predictive and descriptive machine learning models can be conjured.

What is needed for a study?

The study must consist of one thing, data; Sometimes a business question(s) or task that’s a speculative candidate for automation.

Each study takes approximately one month to complete and will be completed in the order in which it was received.

Product: Dashboard Development

Data Visualization via Dashboard Development: Create visually appealing and interactive data visualizations and dashboards to help clients explore and communicate insights from their data. This can include building custom dashboards using tools like Tableau or Power BI.

Product: Custom Machine Learning Models

Predictive Analytics and Forecasting: Provide predictive analytics and forecasting services to help clients leverage historical data to make predictions about future trends, behaviors, and outcomes. This can involve building machine learning models to forecast sales, demand, customer behavior, or other key metrics.

Natural Language Processing (NLP) Solutions: Develop NLP solutions to help clients analyze and extract insights from unstructured text data, such as customer reviews, social media comments, or news articles. This can involve tasks such as sentiment analysis, topic modeling, and text summarization.

Recommendation Systems: Build recommendation systems to help clients personalize and improve their product recommendations, content suggestions, or marketing campaigns based on user behavior and preferences.

Time Series Analysis: Offer time series analysis services to help clients analyze and model time-dependent data, such as stock prices, weather patterns, or website traffic. This can involve techniques such as ARIMA modeling, exponential smoothing, or seasonal decomposition.

Anomaly Detection: Provide anomaly detection solutions to help clients identify unusual patterns or outliers in their data that may indicate potential fraud, errors, or security threats.

prices

Product	Customers<50	Customers >50	Customers>100	Customers >1000
Feasibility Study	$10k	$25k	$20k	$30k
ML  (per model)	$100k	$250k	$999k	$5,ooo,ooo
Dashboard Generation	$3k	$5k	$7k	$10k
Data Mining for Court Cases	$10k	NA	NA	NA