Business Success with Data Analytics

Data and advanced analytics have arrived. Data is becoming ubiquitous but several  organizations are struggling to use data analytics in everyday business process. Companies who adapt data analytics in the truest and deepest levels will have a significant competitive advantage, ; those who fall behind risk becoming irrelevant. Analytics has the potential to upend the prevailing business models in many industries, and CEOs are struggling to understand how analytics can help.

Here are 10 key points that must be followed to succeed.

1. Understand how Analytics can disrupt your industry
2. Define ways in which Analytics can create value & new opportunities
3. Top managers should learn to love metrics and measurements
4. Change Organizational structures to enable analytics based decision making
5. Experiment with data driven, test-n-learn decision making processes
6. Data Ownership must be well defined & Data Access must be made easier
7. Invest in data management, data Security & analytics tools
8. Invest in training & hiring people to drive analytics 
9. Establish Organizational Benchmarks for data analytics
10. Layout a long term road map for business success with Analytics

Top Challenges Facing AI Projects in Legacy Companies

Legacy companies which have been around for more than 20 years have been always slow to embrace new technologies & the case is also very true with embracing AI technologies.

Companies relutcantly start few AI projects - only to abandon them.

Here are are the top 7 challenges AI projects face in legacy companies:

1. Management Reluctance
Fear of Exacerbating asymmetrical power of AI
Need to Protect their domains
Pressure to maintain statusquo

2. Ensuring Corporate Accountability 
Internal Fissures
Legacy Processes hinder accountability on AI systems

3. Copyrights  & Legal Compliance 

• Inability to agree on data copyrights
• Legacy Processes hinder compliance when new AI systems are implemented

4. Lack of Strategic Vision

• Top management lacksstrategic vision on AI
• Leaders are unaware of AI's potential
• AI projects are not fully funded 

5. Data Authenticity

• Lack of tools to verify data Authenticity
• Multiple data sources
• Duplicate Data 
• Incomplete Data

6. Understanding Unstructured Data

• Lack of tools to analyze Unstructured data
• Middle management does not understand value of information in unstructured data
• Incomplete data for AI tools

7. Data Availability

• Lack of tools to consolidate data 
• Lack of knowledge on sources of data
• Legacy systems that prevent data sharing 

Data Life Cycle Management in the Age of Big Data

Organizations are eager to harness the power of big data. Big data creates tremendous opportunities and challenges. 

The data lifecycle stretches through multiple phases as data is created, used, shared, updated, stored and eventually archived or defensively disposed. Data lifecycle management plays an especially key role in three of these phases of data’s existence:

1. Disclose Data
2. Manipulate Data
3. Consume Data

Organizations can benefit from data only if they can manage the entire data lifecycle, focus on good governance, use, share and monetize data.