Data science often boils down into processing large volumes of data in order to extract actionable insights that the management team of an organizations can leverage to adjust the direction of the business . However, a major problem in data science is that before actionable insights could be extracted from the data, many technical challenges must be overcome. These challenges often include the collection, cleaning, and integration of data, as well as setting up and using Big-Data number crunching tools such as MapReduce, Spark, and etc. In fact, studies have already found that more than 80% of the time of a data-science project is spent on these preparative actions.
We in DipperX we offer the following services in order to help you focus merely on the extraction of actionable insights from your data instead of wasting time on technical challenges:
Advice on what tools and technologies to use: There are a growing set of tools and technologies that can be used for data-science projects, including data extraction software, scalable data warehouses, large-scale number-crunching systems, and etc. We can analyze your problems and advice you on what technology suits best your use cases.
Scraping & crawling: Our team has years of experience on building custom crawling and scraping tools to extract data from the public web.
Digitization of old internal documents: We can help you convert your legacy documents that are piled into decades old folders to digital format that could be used in your data science projects
Data Cleaning & Integration: Data science projects usually include data from numerous sources and varying formats and qualities because most valuable insights often manifest only after multiple datasets are integrated with each other. Consequently, in every data science project, a considerable amount of time is spent on the collection, cleaning, transformation and integration of datasets. Although this process poses more technical challenges that intelectual ones, very often companies make the mistake of assigning data scientists to this task. This is a wasteful allocation of the intellectual capacities of data scientists who must focus on creative ways to extract actionable insights from the data rather than wasting their time on removing noise from the data or figuring out how to integrate two datasets. While data engineers and data scientists need to be work in close collaboration with each other, it is important that highly skilled and technical data engineers are assigned to do cleaning and integration of the data while data scientists mostly focus on analytical tasks of extracting insights from the data. Our experienced team of data engineers in DipperX, can work in close collaboration with the data science team to free data scientists from the burden of data cleaning and integration and accelerate data science projects.
Data Transformation: Choosing the right database is one of the most important decisions that any technology company must make because in many tech businesses, any user action eventually leads to a read/write operation from/to the database. As a result, the performance and scalability of the database could easily become a major factor in the performance and the scalability of the whole system. There are often many types of databases the companies can choose from including: relational databases, document-based databases, column-based databases, graph databases, and etc. Each of these databases have their own advantages and disadvantages and to choose which largely depends on the use-cases. Hence, it is important that the right database is chosen in the beginning as wrong choices could lead to considerable underperformance and low efficiency. Unfortunately, sometimes companies make the wrong choice in the beginning and need to transform their databases in later stages. The transformation is delicate process and requires high technical skill and understanding of both databases. and Our expert consultants in DipperX can help you both in choosing the right database if your company needs to select a database now as well as in the database transformation.