Written by admin

March 10, 2018

Data – pieces of information, whether through observation, tribal knowledge or empirical collection, has been used and collected throughout history to form the foundation of decision making.

Today, inexpensive storage, sensors, smart devices, social software and cloud computing have democratized data collection to the point where organizations are collecting it from every conceivable channel.

Value from this high-quality data is nearly limitless. The challenge in most cases becomes tapping into the sources today’s organizations collect from and to extract reliable chunks of insight to be processed into analytics.

Analytics can create new opportunities and disrupt entire industries, but few leaders appreciate how. To maximize the opportunity, organizations must move from hoarding data to sharing it. Many have begun to pool data as part of industry consortia, realizing that there is more value in comprehensiveness.

Leading organizations are now including big data from both within and outside the organization, incorporating structured and unstructured sets, machine derived, and online and mobile data to supplement and build on the basis for historical and forward-looking views.

AviaMind works with both leading and lagging organizations from starting to capture data all the way to applying advanced algorithms. Analytics create value when big data and algorithms are applied to yield a solution that is measurably better than it was before.


An expression from the earliest days of computing, “Garbage In, Garbage Out”, is more relevant than ever as the amount of available data grows. Data science and data engineering are two distinct disciplines; however, industry commonly uses them interchangeably. Lets establish the unique identities and illustrate how we treat the major differences between Data Science and Data Engineering:

Data Science

Data Engineering


The discipline that develops a model to draw meaningful and useful insights from the underlying data.

The discipline that takes care of developing the framework for processing, storage, and retrieval of data from different data sources.


Requires an expert level knowledge of mathematics, statistics, computer science and domain. Hardware knowledge is not required.

Requires programming, middleware, and hardware related knowledge. Machine learning and Statistic knowledge is not mandatory.


Data Science is responsible for developing models and procedures for extracting useful business insights from the data.

Data engineering is responsible for discovering the best methods and identification of optimized solutions and toolset for data acquisition.


Data product

Data flow, storage, and retrieval system.


Airline revenue and pricing strategy for peak or holiday scheduling.

Data pulled from tweets and social media campaign pipeline into the hive data warehouse.

AviaMind combines subject matter expertise within data science and engineering to translate raw data into answers to our client’s most difficult challenges.