What Role Does Data Strategy Play In Advanced Analytics?

Advanced analytics has had a long road to development, with numerous roadblocks along the way. Data gathering technology, data purification processes, and advanced analytics talent assistance are some of the most challenging parts of data analytics that still exist today. It took years to get to the automated age of business analytics, when even non-technical business users can use AI or machine learning-enabled self-service analytics.

One of the least known and vital components of Advanced Analytics, according to many experts, is how to deal with the data assets themselves. Business analytics will continue to struggle to execute best practises without strong data privacy, data security, and data governance rules in place. This can only happen if a company has developed a good Data Strategy to enable well-governed Business Analytics.

Businesses Have Faced Primary Data Challenges in the Past

Business users can never derive any value from advanced analytics or BI activities unless the data they use is clean, safe, and well-governed, no matter how complex or self-driven an enterprise business intelligence (BI) system is.

Cleaning and processing data gathered from different sources such as social, mobile, sensors, and online logs has always been the key challenge. With the rise of Big Data and associated technologies such as Hadoop and IoT, the Data Management dilemma has gotten more difficult. A lot can happen on the analytics side now that advanced predictive and prescriptive analytics technologies are available in the hands of average business users, but it isn’t occurring due of poor Data Quality.

Data Management Trends in 2020, a DATAVERSITY® article, explores how to effectively deal with Data Management practises, such as Data Governance, as a core driver of data-driven cultures in enterprises. For analytics to succeed in an organisation, data governance must be governed by unified policies.

Furthermore, while many business users wish to employ predictive analytics, there is a severe scarcity of skills in using the more complicated or advanced technologies incorporated in BI platforms. Despite the fact that the 80/20 rule has been abandoned, most corporate users are unaware of how to use modern BI and analytics technologies. How Can Machine Learning Affect Your Organizational Data Strategy? was published in the article How Can Machine Learning Affect Your Organizational Data Strategy? demonstrates how a strong Data Strategy may aid Machine Learning algorithms in delivering business analytics solutions.

Making self-service BI or analytics tools truly self-driven is the third hurdle. According to research, many business users using self-service platforms want the assistance and support of qualified data scientists or technologists to complete their everyday tasks. The mainstream adoption rates of self-service platforms are expected to alter over time.

The author of the Deloitte report The Analytics Advantage We’re Just Getting Started is concerned about how little business data is used in corporate decision-making. According to the research, data analytics operations are not fully aligned with corporate decision-making, and such alignment can only occur if managers intentionally build a “analytics culture.”

Furthermore, analytics methods must be centralised and governed in order for the results to be readily accessible across divisions or departments. This is an area where an organization’s entire Data Management strategy can make a big difference. In fact, when analytics procedures are linked to an organization’s Data Strategy, firms may reap the full benefits of analytics.

What is a Data Strategy for an Organization?

In its most basic form, data strategy refers to a documented plan for improving and protecting data quality, data security, and data access across an organisation. A thorough Data Strategy can also include plans for producing new revenue streams from data, as well as plans for exploiting data to gain a competitive edge. As a result, a well-developed Data Strategy executed in an organisation will contain data management systems, procedures, rules, and standards.

For technologies like big data to succeed, a solid Data Governance programme is critical, and that’s where Data Strategy comes in. The term “Data Strategy” is used in the corporate world to describe a well-balanced combination of organisational, technical, and compliance procedures that improve data trust. According to the Salesforce article Strengthen Your Business Intelligence with Data Strategy, the key to data analytics in enterprises is effective Data Strategy or Data Governance, and over 70% of executives believe that a separate business unit should be developed to handle data goods or services.

Analytics for Actionable Insights: Transforming Data into Intelligence

Advanced analytics is quickly becoming a significant differentiator for firms in an increasingly competitive business environment, and organisations can no longer afford to ignore it. Businesses utilise data analytics to better understand their customers and explore new markets, for example. Data-driven, educated decisions can spell the difference between success and failure.

According to the International Data Corporation’s Worldwide Semiannual Big Data and Analytics Spending Guide, analytics is growing increasingly popular, with spending expected to rise from around $122 billion in 2015 to over $187 billion in 2020, or roughly a 50% increase in five years.

Despite these hopeful statistics, the Deloitte Report states that poor data quality, a lack of expert skills, inadequate IT infrastructure, and a lack of management support are the most significant impediments to mainstream adoption of data analytics.

Surprisingly, the author of the article Setting Up for Success with Advanced Analytics correctly emphasises that IT teams frequently sell “analytics” to top management on the merits of associated technologies such as Data Science or machine learning, but they forget to mention a strong Data Strategy, which ultimately controls the analytics process’ success.

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