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

Big Data is paving the way to the emergence of Smart Data. Huge, differentiated and big volumes of Big Data practically needs smart data for its everyday working because they facilitate -
• Unstructured and structured data aggregation and analytics
• Simplified and accelerated data modeling
• Access and data governance
Big Data is inexorably transforming into smart data. It is the preferred technology used in the diverse application of Big Data including the Internet of Things, Cognitive Computing, Semantic Graph Databases, Data Lakes and Artificial Intelligence.
The nature of Smart Data represents an insurgence in the logic applied to data driven processes. Big Data is important in Data Management as it has the ability to implement action from real-time analytical insight and consolidate all of one’s data in the process. Applications such as Internet of Things automate processes that would otherwise take too long. In context of Smart Data’s ability to increase the utility of Data Lakes is its ability to help clarify the sort of role-based access that is a pillar of proper Data Governance. Smart Data Modeling is preferred in analytics because there is a degree of flexibility and agility in the modeling required for Smart Data that exceeds non-Semantic Data. Along with its advantages for analytics, application development, data integration and Big Data Governance, Smart Data’s reconfiguration of transactional data will establish the fact that Big Data is surely evolving into Smart Data.

Read more at: http://www.dataversity.net/the-evolution-of-big-data-to-smart-data/

Big data developments in 2015 show significant change in terms of data storing as well as data analyzing techniques. Measuring data agility is becoming more and more popular, as opposed to the conventional system of storage and management of data resources. The investment and scope of big data projects will be directly related to the impact and of an organization's response to changing trends among consumers, markets and competition. Another big trend organizations will focus on in 2015 is usage of data lakes. Using data lakes, Hadoop and BI systems and cloud computing, real time data analysis and decision making is expected to be fast. So the dynamic change in technology will bring revolution in terms of business decision making. Read more at: http://channels.theinnovationenterprise.com/articles/advancements-in-big-data-in-2015 

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