Data analytics, the science of examining raw data & drawing conclusions from it; has helped organizations, businesses and companies to gain a lot of insight for deriving policies and management decisions, along with achieving their mission, vision and goals more effectively and efficiently.
However; the sad part is that still decisions are made, which are not based on data in some or the other way. There are two major reasons responsible for it. First one is that they do not hire data analytic experts. Second one, which is more prevalent, is that they do not have their raw data in place. Due to this, data analytic service providers are not able to assist them in providing required insight from the data. On top of it, the steady increase in the size and complexity of data pose consistent challenge to these organizations to cope up with the requirements.
Let’s check out on some of the challenges these organizations face while managing their data:
1. Challenges of existing data
Most of the organizations get stuck when it comes to organizing the existing data. We know that data sits in nooks and corners of every workstation and desk in any organization with different owners and silos. Prima fascia solution to this challenge is to address it with help of IT department, where they add more hardware or may be by designing custom applications to access and analyze the information. But time and again it has proved its inefficiency, while accessing required information; making the staff drown in a pool of irrelevant data.
2. Data in various structures
Next that comes in to picture is data in various structures. Certain formats require more time to get analyzed as compared to others; and most of the times – IT teams are not aware of the best and shortest way to turn that data into insights. So, on one hand where some information is conveniently accessible from related databases, semi-structured or unstructured data is not as easily accessible and hence not analyzed.
- Semi-structured data
This type of data does not conform to most of the data models in relational databases. It is because they contain tags & markers which help in separating certain or a few of the elements. Emails are the best example to understand this. Though emails do have timestamps and IP addresses so as to get categorized into semi-structured data, most of the emails data is unstructured content or text that is been written.
- Unstructured Data
Usually this type of data is not at all organized in a predefined manner and is text-heavy all the time. At times it may also contain unstructured data such as dates, numbers, and facts; but all said and done this unstructured data is really difficult to sort. Most of the information is based on free-flowing content. So net-net analyzing unstructured and semi-structured data consumes a lot of time and effort to conclude what is useful and what is not.
3. Resource constraints
This proves to be a final nail in the coffin. Any organization seeking assistance from data analytic services provider, faces this challenge of constrained resources, may it be finance or manpower. Fluctuating economies have compelled businesses to have limited budgets, and on top of it, whatever is assigned is mainly consumed to fund dedicated hardware data storage and developing custom applications.
Data analytics seem daunting; how could data processing help it succeed?
Datasets are known for their size, various sources and representation of multiple attributes. Analytics should be able to access all the data sources & formats. As we all know, real world data is more inclined towards being incomplete, inconsistent and noisy. This makes data preparation clubbed with data cleansing, data integration, data transformation and data reduction, some of the most important procedures for data mining and data storage.
Data cleansing has proved its worth for filling up missing values, smoothen the noisy data, identification of outliers and correction of data inconsistencies. Combining data from multiple sources to form that logical data storage is taken care of by data integration process. Data transformation is the activity which segregates the data into appropriate forms to make data mining convenient and meaningful. This is then followed with data reduction in order to obtain a reduced representation of the data while ensuring there is no loss of information. Data analytics preceded by data processing is capable of streamlining analytical processes to improve performance, efficiency and business outcomes.