Predictive Analytics, Artificial Intelligence, bots, data science – the waves of advances in data science keep on coming. Access to old data and not skill base or technology, turns out to be the biggest obstacle for powerful analysis insight which requires a tedious data preparation. Data Silos are something of a buzzword, a demon lurking in the enterprise which makes it prohibitively costly to extract data and makes company initiatives nearly impossible. Silos lead on to limited information, redundant data and interdepartmental inefficiencies. To make the data streamlined, accessible and impactful to the organization’s bottom-line, the development of silos must be mitigated in a progressive and pragmatic approach. Things aren’t as beautifully simple as the buzzword “data lake” might conjure. A combination of various methods including use of the right software, encouraging proactive communication, blurring departmental descriptions and roles coupled with the goal of integration at the background would lead to an integrated platform thus overcoming the problem of data silos. Focus on Wide Data Analytics and not only big data, stands indispensible to achieve a future state of mature analytical competency, however, silos aren’t entirely evil in the context of data management.

Read More at: https://smartdatacollective.com/how-to-eliminate-silos-in-company-wide-data-analytics/

Big data has now become old, organizations are very well familiar with it. But some of them are still struggling with data. Data lakes provides easy access of data and data mining. Due to management defaults data may turn into data swamps making analysis difficult. Data Lake has a lot of benefits, but the data growing in size becomes difficult to handle. To avoid this problem following steps are taken at the time of creation. 1) Too much data must not be collected at the beginning. 2) Data insighting cannot be done manually, so machine-learning capabilities should be enabled. 3) Businesses should keep an eye on changing data statistics and the employed models. To make it successful one needs to integrate it with business strategy and outcome. Read more at: https://www.readitquik.com/articles/elastic-computing/smart-ways-to-manage-your-big-data/

A data lake is a single repository (Hadoop or another NoSQL platform) that accesses and stores all types of data, i.e. structured or unstructured, enabling authorized users to quickly access data from one place. A data lake also captures changes to data. It should be a part of an enterprise data storage strategy for getting the most value from your organization’s data. Nowadays, data lakes consist of machine-generated logs and sensor data, raw customer data collected from website clicks, social media, collections of documents such as e-mail and customer files, and geo-location traces. To know more about data lakes & its benefits, follow:  http://it.toolbox.com/blogs/it-solutions/what-a-data-lake-isand-what-it-should-do-69978

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. According to Gartner, the advantage of Data lakes is: helps in addressing the old and new problem by providing the relevant set of data for analyzing the situation. Disadvantages are:

• Lack of data quality.
• Security and access control.
• Data Lake requires proper infrastructure.

But using purpose built cloud systems security, access control and scalability problem can be solved, but data quality is not good.
 To know more about Data Lake and its advantage and disadvantages, read an article Data Lakes: Emerging Pros and Cons by Joe Panettieri. Link: http://www.information-management.com/news/Big-Data-Lakes-Cloud-Computing-Analytics-10026889-1.html