The journey of advanced analytics has been a long in development, with many hurdles along the way. Some of the most complicated aspects of data analytics that still remain today are data gathering technologies, data cleansing methods, and skill support for advanced analytics. It has taken years to come to the automated age of business analytics, when even mainstream business users without much technical knowledge are able to use AI or machine learning-enabled self-service analytics.

Many experts agree that one of the least understood and crucial ingredients of Advanced Analytics is how to deal with the data assets themselves. Without proper data privacy, data security, and Data Governance policies in place, business analytics will continue having a difficult time in implementing best practices. This can only happen when a sound Data Strategy has been designed in an enterprise to enable well-governed Business Analytics.

Primary Data Challenges Faced by Businesses in the Past

No matter how sophisticated or self-driven an enterprise business intelligence (BI) system is, business users can never extract any value from their advanced analytics or BI activities unless the data they are using is clean, secure, and well-governed.

Traditionally, the main challenge has been cleaning and preparing the data gathered from disparate sources like social, mobile, sensors, and web logs.  The Data Management challenge has become more complex with the emergence of Big Data and associated technologies like Hadoop and IoT. Now with advanced predictive and prescriptive analytics tools available in the hands of ordinary business users, a lot can happen on the analytics side, but it is not happening because of the poor Data Quality.

The DATAVERSITY^® article Data Management Trends in 2020 discusses to how better deal Data Management practices, which include Data Governance, as a core driver of data-driven cultures in organizations. Data Governance must be managed through unified policies for analytics to succeed in an organization.

Furthermore, though many business users want to indulge in predictive analytics, there is a tremendous skills shortage in using highly complex or advanced tools embedded in BI platforms. Although the 80/20 rule has been discarded, in reality, most business users do not know how to take advantage of advanced BI or analytics tools. The article How Can Machine Learning Affect Your Organizational Data Strategy?  demonstrates how a sound Data Strategy can help Machine Learning algorithms deliver results in business analytics.

The third challenge is making self-service BI or analytics platforms truly self-driven. Research states that in many cases, business users seek the help and support of qualified data scientists or technologists to get their daily work done on self-service platforms. With time, the mainstream adoption rates of self-service platforms will hopefully change.

The author of the Deloitte Report The Analytics Advantage We’re Just Getting Started expresses grave concern about the low use of business data in corporate decision making. The report indicates that data analytics activities are not fully aligned with corporate decision-making, and that alignment can only happen when an “analytics culture” is consciously cultivated by the managers in an enterprise.

Moreover, analytics processes must be centrally controlled and governed to make the outcomes widely available across the divisions or departments. This is an area where overall Data Management strategy of an organization can play a key role. In fact, businesses can reap full benefits of analytics when the analytics processes are tied up with organizational Data Strategy.

What is an Organizational Data Strategy?

Data Strategy, in the simplest definition, indicates a formal plan for improving and preserving Data Quality, data security, and data access across an enterprise. A comprehensive Data Strategy can also outline plans for generating additional revenue streams from data, or plans for using data for competitive advantage. Thus, a well-developed Data Strategy implemented in an organization will include architectures, processes, policies, and standards related to Data Management.

A good Data Governance program is vitally important for technologies like big data to succeed, and that is where Data Strategy comes in. In the business world, the term “Data Strategy” indicates a judicious combination of organizational, technical, and compliance measures to elevate the level of trust in the data. The Salesforce article Strengthen Your Business Intelligence with Data Strategy indicates that optimal Data Strategy or Data Governance is the key to data analytics in businesses, and almost 70 percent of executives feel that a separate business unit should be created to handle data products or services.

Turning Data into Intelligence: Analytics for Actionable Insights

Organizations cannot afford to ignore advanced analytics anymore as analytics is fast becoming a key differentiator for businesses in an increasingly competitive business market. Data-driven, informed decisions can mean the difference between success and failure, for example, businesses use data analytics to know their customers better and to explore new markets.

A study Worldwide Semiannual Big Data and Analytics Spending Guide from International Data Corporation proves that Analytics is becoming more and more popular, and is slated to grow from about $122 billion in 2015 to over $187 billion in 2020, which roughly boils down to a 50 percent growth is a five-year period.

In spite of these encouraging data points, the Deloitte Report notes that the biggest barriers to widespread adoption of data analytics are poor Data Quality, lack of expert skills, inadequate IT infrastructure, and lack of management support.

Surprisingly, the author of the article Setting Up for Success with Advanced Analytics aptly stresses that IT teams often sell “analytics” to the top management on the merits of the associated technologies like Data Science or machine learning, but they forget to mention a strong Data Strategy, which finally controls the success of the analytics process.

Data Analytics in Use in Enterprises: Some Examples

• Example 1: According to the Deloitte Report Pricing Analytics The three-minute guide, the pricing of products and services shape the future profit potentials of a business, and properly implemented pricing policies can improve the margins by up to 7 percent in a calendar year, which translates to an ROI of 200 to 350 percent. Thus, the insights gained from pricing analytics are invaluable in shaping the price structures.
• Example 2: At Dell, predictive churn modeling is used to retain customers and reduce churn. They compare social data with other data sources to identify customers at risk and then take adequate preventive measures.
• Example 3: Dell’s Lifesys platform is used to reduce customer churn with the help of embedded analytics available within Lifesys, Dell’s platform for insurance claims processing.
• Example 4: The PWC post indicates that a pharmaceutical company uses a home-grown “deal intelligence” platform to get financial insights at a granular level before closing a deal.

The Impact of Big Data on Organizational Data Strategy

According to the EY market report, big data provides a strategic advantage over traditional business analytics methods as it allows users to collect and store massive amount of multi-structured data at a relatively low cost. The “volume, variety, and velocity” of multi-channel data is no longer a threat to organizations, as Big Data technology can smoothly handle data at scale.

Then what is missing? Though big-data enabled analytics promise many benefits to businesses, here are some inherent risks involved with the use of big data:

• Architecture-related Risks: While big data does not restrict the scope and structure of data storage platforms, high-volume data can cause data redundancy, bad Data Quality, and Data Governance issues. Integrated data structures can cause data linkage and relevance problems. Finally, skills shortage around big data architectures can be on on-going issue.
• Governance Risks: Very sound data strategies like data ownership and control need to be formulated and put in practice for the data to be of high quality, well governed, and secure.
• Management-related Risks: As big data provides easy and economical access to multiple data sources, the risks of “noise” and data pollution is present. If management is not ready in terms of implementation, support, and training of big data, then such problems will hinder analytics activities in the enterprise.
• Technical-capability Risks: Big data experts are highly skilled and experienced individuals; not everyone can fit into their shoes. Enterprises will have to rethink their training and project strategies to ensure that the appropriately skilled big data experts are present in every analytics team.
• Usage Risks: Though data integration and distributed processing power can help analytics across the enterprise, skills shortage will again be an issue. The organizational manpower can easily get overloaded with high-volume data and useless information.
• Quality Risks: Quality monitoring is an ongoing concern for multi-structured, multi-channel data, and rectifying errors can be costly. Companies will have to build competent and efficient teams to manage Data Quality of big data. Existing Data Governance models may have to be tweaked to embrace new types of data.
• Security Risks: Security has always been a major concern for any type of business data, so enterprises need strong and coherent security policies as part of the overall Data Strategy to build trust among the data community. Related technologies like the cloud, or mobile may put big data at risk of piracy or corruption, but only regulations and policies can curb these negative effects.
• Privacy Risks: Technological methods like encryption, key-coding or data-sharding have been traditionally used to preserve data privacy, but in case of big data, existing policies will have to be reviewed and modified based on perceived privacy risks.

All of the above indicate that an organization needs a strong Data Strategy to engage in meaningful advanced analytics activities. Without such a strategy the ability to enact enterprise-wide analytics remains unfeasible for most organizations.

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