Importance of data science and analytics in decision-making.
Key elements: collection, cleaning, analysis, visualization, interpretation.
- Data Quality & Availability
- Poor Integration
- Model Interpretability
- Scalability
Overcoming challenges with modern solutions
How Prescience helps businesses.
As businesses today are generating vast amounts of data, it has become important to integrate data science and analytical techniques to derive the right information from it. Modern techniques like AI, ML algorithms, statistical analysis, etc. are used to find hidden patterns in data. However, despite the vast potential of data science and analytics, it comes with its own challenges. Businesses need to address these challenges carefully to make accurate and smart decisions.
In this blog, we will explore four common challenges faced and the solutions to tackle them.
Understanding Data Science and Its Elements
Data science is an area that focuses on deriving insights from raw data. It is an interdisciplinary area that uses methods from computer science, statistics, and domain-specific expertise to glean insights from both structured and unstructured data. The elements of data science include:
- Data collection – Compiling raw data from various sources including web, databases, and sensors
- Data cleaning – Once this raw data is received, it needs to be processed, handling missing numbers, removing duplicates, correcting errors, and so on.
- Data analysis – It is the process of finding patterns and relations between data using statistical and machine learning methods.
- Data visualization – Once we derive the insights, they need to be visualized in a way to effectively communicate conclusions.
- Interpretation – To use analysis and make final data-driven conclusions.
Challenges Faced in Data Science and Analytics
Data Availability and Quality
Ensuring data quality and availability is a major challenge in data science and analytics. Poor data quality will lead to inaccurate decisions. Poor data will be inconsistent, not reliable, have missing values, etc. For instance, a common data quality issue in logistics could be a ZIP code in a database that does not match the customer’s address, leading to delayed deliveries. Inconsistencies in date and time formats make it difficult to analyze data.
Additionally, availability of data is also a problem faced within businesses. It could happen mostly due to factors like excessive costs and time, privacy concerns related to sensitive data, etc. Data scarcity can also happen when sometimes similar data is stored in different formats like names, alternative spellings, or nicknames. A strong data governance strategy, with data processing, validation, and cleaning can help in getting accurate insights.
Poor data integration
To analyze data, a uniform view of data is important, where we have all the data processed at one place. Businesses collect raw data from various sources which might be inconsistent and incomplete. This will cause an issue while integrating it into another computing platform or to any AI model. Businesses can implement a standardized process of ETL (extraction, transformation, and loading) by ensuring a uniform format storage in a central repository.
Moreover, today businesses are heavily relying on storing data securely with data lakes and warehouses like Snowflake, Google Big Query, or AWS Redshift to store data safely, which could be used at any time of integration.
Model output interpretability
Model outcome interpretability is a major challenge with today’s complex neural networks like deep-learning and machine learning. The AI model will take data, process, and predict the outcome. But it is often difficult to see exactly where this output is derived from. This is called the “black box” problem, where we do not know how the model predicted the outcome. Traditionally, the ML models were simple like decision trees, where it was easy to analyze how the model made the prediction.
Explainable model techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) help explain which input or dataset is the primary reason for the model’s decision. Additionally, tracking model drifts, meaning shifts in data patterns over time, can refine model interpretation and maintain the model’s relevancy.
Data Scaling Ability
With businesses generating large amounts of data every day, the volume of data is also increasing. Businesses with traditional computing infrastructure often struggle to manage large volumes of data. Thus, scalable data platforms are required to manage high volumes of data. Today, businesses are depending heavily on cloud data platforms for scalability. Some of the cloud data platforms are AWS, Google Cloud, and Azure.
When data drift happens over time, it leads to inaccuracies. To retrain the business models periodically, it incurs training costs. As a solution, cloud data provides scalable data platforms with appropriate costs.
Conclusion
As data science and analytics are important for businesses today, it is equally important to identify these challenges and overcome them. By using modern techniques, scalable platforms, and ethical practices, businesses can transform raw data into meaningful insights.
We at Prescience Decision Solutions navigate the complexities related to data science and analytics across various industries like sales, finance, e-commerce, marketing, etc. by delivering custom solutions that integrate intelligent models to overcome challenges while ensuring data quality, transparency, and scalability. Moreover, our Data Sentinel solution helps organizations achieve superior data quality without disturbing the existing infrastructure.
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Prescience Team
