Computer Science, asked by christinearapan, 1 month ago

What should a company do to develop a better data culture?
a. Have the Data Architects redesign the engineering processes and responsibilities so that the Engineers will be responsible for the data management of the company.
b. Designate time to think about and define the “art of the possible”, so they can feel more confident about the different methods of using their data.
c. Ensure that everyone in the company understands the importance of data and are aware of what data can do to improve business processes.
d. Show how the company's data usage will appeal to the human aspects of trust, ownership, and ethical use of their employee data.

Answers

Answered by adityarajsinhgohil99
0

Answer:

Summary. For many companies, a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making. Why is it...more

Exploding quantities of data have the potential to fuel a new era of fact-based innovation in corporations, backing up new ideas with solid evidence. Buoyed by hopes of better satisfying customers, streamlining operations, and clarifying strategy, firms have for the past decade amassed data, invested in technologies, and paid handsomely for analytical talent. Yet for many companies a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making.

Why is it so hard?

Our work in a range of industries indicates that the biggest obstacles to creating data-based businesses aren’t technical; they’re cultural. It is simple enough to describe how to inject data into a decision-making process. It is far harder to make this normal, even automatic, for employees — a shift in mindset that presents a daunting challenge. So we’ve distilled 10 data commandments to help create and sustain a culture with data at its core.

Answered by devikarana1212
0

Answer:

collection of insights and perspectives

1 Foreword

The role of analytics and technology in the rise of intelligent operations

4 The impact of technology on business process operations

10 Data-to-insight-to-action: Taking a business process view for analytics to deliver real business impact

22 Built to adapt: A better business technology approach for volatile times

24 "Robotics" in process operations: Useful rapid automation, no transformation panacea

28 Loyalty 2.0: Industrialized analytics embedded in your processes

34 The changing face of business collaboration

48 Almost plug and play: How advanced process operations help M&As

52 A rigorous business case for advanced operating models

Advanced finance and accounting operating models

62 Transforming finance and accounting through advanced operating models

68 Debunking the myth of leveraged AO-FAO solutions

72 Augmenting the FAO technology landscape – Exploring new engagement models

78 Master data management is the next big thing—Seriously

82 Separating impact from hype: How CFOs achieve technology ROI

86 Continuous transaction monitoring: Using analytics to detect fraud or simple payment errors in real time

Advanced procurement and supply chain models

94 Transforming procurement operations through advanced operating models

98 "Industrialization" of sourcing and procurement operations

104 The supply chain: The CFO's crystal ball

106 Tail-end spend: Reaping significant savings with the right operating model

Advanced operating models in banking and financial services

Commercial banks look for talent and technology as they strive to industrialize operating models

The retail banking industry sees changes in leadership as banks pursue growth and new operating models

Transforming banking operations through advanced operating models

Transforming risk management in the financial sector through advanced operating models

Predictive analytics model validation: Building a model validation group to better mitigate risk

Remediation as a Service: A cost-effective response to regulatory and strategic change

Multi-dimensional time series-based approach for banking regulatory stress testing purposes: Introduction to dual-time

dynamics

How big data will transform the equipment finance industry

A 38% increase in production costs drives new operating models through business process outsourcing

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