Data strategy consulting services

Data strategy

Turn your data into measurable business outcomes

Book a consultation

See how we work

The problem

Your data is your most valuable asset. Right now, it might be your biggest blind spot.

You have more data than ever, but the decisions that matter still rely on gut instinct, stale reports, or numbers that different teams define differently. AI initiatives stall because the foundation is not ready. Compliance audits take weeks of manual evidence gathering. And the cost of maintaining fragmented platforms keeps rising while the value they deliver remains flat.

You do not need more data. You need a strategy that connects what you have to what you are trying to achieve.

Solution overview

A data strategy designed for execution, not a shelf

Tenjumps builds data strategies that move straight into production. We start with your business goals, assess where you are today, and deliver a roadmap that connects your data assets to measurable outcomes. Every engagement is designed to produce results in weeks, not quarters.

01

Discovery and ideation

We define your enterprise data vision and build the roadmap to get there. This starts with stakeholder alignment, goal definition, and identifying the highest-value opportunities where data can drive immediate business impact. We design the solution architecture from the start so strategy flows directly into execution.

02

Data maturity assessment

We evaluate your current data infrastructure, governance practices, team capabilities, and tooling against enterprise benchmarks. The result is a clear, honest picture of where you stand and a prioritized set of actions to close the gap between where you are and where your business needs to be.

03

Data monetization

Your data has unrealized business value. We help you identify how to convert existing data assets into revenue streams, cost reductions, and competitive advantages through better analytics, new product insights, or operational intelligence. This is about making data pay for itself.

04

Technology and data audits

We conduct a thorough review of your current technology stack, data architecture, and operational workflows to identify technical debt, security gaps, compliance risks, and cost inefficiencies. You get a clear action plan with prioritized recommendations and estimated ROI for each fix.

What our clients will see

Days to production systems

Days to production

8-30 days

Average ROI

8 month

Efficiency gains

60%+

Improvement in data accuracy

90%+

90%

Why companies choose Tenjumps for data strategy

Tenjumps builds AI systems designed for production from the start. We connect AI to your data foundation, engineer the full model lifecycle, deploy with governance and compliance built in, and automate operations so your AI investment scales. Every engagement is grounded in real business use cases, not technology demos.

Senior engineers from day one, not after the sale

Most consultancies hand you a strategy deck and walk away. We design the strategy and build the systems. One team from roadmap to production means no translation gaps, no ramp-up delays, and no finger-pointing between vendors.

30+ years of individual experience, deployed in weeks

Our teams average 30+ years of experience building enterprise systems through every major technology evolution. That depth means we move faster because we have solved these problems before. Production systems in 8-30 days, not 8-30 months.

Built to hand off, not to lock in

Every engagement includes knowledge transfer, documentation, and team enablement. Our goal is to make your internal team self-sufficient, not to create a dependency on Tenjumps.

Success stories

Results that speak for themselves

60%

of tickets resolved instantly

Customer service automation

The Challenge: A logistics leader was overwhelmed by 150+ daily emails—83% of which were repetitive shipping queries.

The Solution: Tenjumps deployed an AI chatbot trained on historic email patterns in just 60 days.

The Result: 60% of tickets resolved automatically without human intervention.

24/7 global support across 200+ countries.
CS reps redirected to high-value, complex cases.

99%

reduction in candidate verification time

HR automation

The Challenge: A financial services firm had a 4-month hiring lag due to manual recruiter verification.

The Solution: We built an agentic AI solution in only 10 days to automate re-engagement and LinkedIn verification.

The Result: 70% candidate re-engagement with 90% matching accuracy.

Delivery time slashed from 4 months to 4 weeks.
Eliminated weeks of manual searching for the team.

Featured

Read our latest insights on enterprise data strategy

How we evaluate, deploy, and govern AI with your team.

From strategy to scale in four stages

Our Business Excellence Model (BEM) takes you from assessment to production systems to continuous improvement. One team owns the entire journey. No handoffs between firms, no strategy decks that sit on a shelf.

01 Explore

Strategy & Readiness

We audit your data foundation and infrastructure to identify high-value use cases. The output is a prioritized roadmap based on technical feasibility and business ROI.

02 Engage

Architecture & Governance

We select the right tech stack—RAG, agents, or ML—and design for scale. For regulated industries, we bake in compliance frameworks and guardrails before a single line of code is written.

03 Execute

Agile Deployment

Our engineering pods build and ship. Whether it’s GenAI agents, MLOps pipelines, or intelligent automation, we deploy with full observability, auditability, and governance from day one.

04 Evolve

Optimization & Autonomy

We monitor for drift, bias, and performance, building feedback loops for continuous retraining. Our goal is to mature your internal AI capability so you own the platform.

Insights from our team

Explore all insights

Cost of poor data quality

A single data quality issue cost 50 engineering hours last quarter. Only 6 were tracked. Paleti Lakshmikanth breaks down where the hidden time goes.

Production data engineering: what tutorials never teach you

Production data engineering looks nothing like tutorials. Kavya Kumari shares what actually changes when pipelines run at scale and stakeholders are waiting.

Cloud cost optimization: The 50GB report nobody reads

For the 50GB weekly export, 47 recipients receive it, but only 3 open it. Bhavya Venu breaks down how wasteful data exports drain cloud budgets and what to do about it.

FAQs about data consulting

What does a data strategy engagement actually deliver?

Every engagement produces a concrete roadmap with prioritized initiatives, estimated ROI, and a clear path from strategy to production. We do not deliver slide decks that require another vendor to implement. Our team builds what we design.

How long does a data strategy project take?

Discovery and assessment typically take 2-4 weeks. From there, we can have production systems running in 8-30 days depending on scope. Our BEM delivery model is designed to show measurable progress within the first month.

We already have data infrastructure. Do we need a strategy?

If your teams define the same metrics differently, your AI initiatives keep stalling, or your data costs are climbing without proportional value, then yes. A data strategy audit identifies what is working, what is creating drag, and where the highest-value improvements are. Many of our engagements start with companies that have infrastructure but lack alignment between that infrastructure and their business goals.

What industries do you work with?

We work across logistics, financial services (BFSI), and manufacturing, with deep domain experience in each. Our data strategy practice is industry-aware but platform-agnostic, meaning we adapt to your specific business context rather than applying a one-size-fits-all framework.

Do you only work with Databricks?

We are a certified Databricks consulting partner and recommend the lakehouse platform for most enterprise data strategies. But we work across AWS, Azure, and GCP, and our strategy engagements are designed to evaluate the right platform for your needs, not to push a predetermined solution.

What does a typical team look like?

We deploy self-contained pods with the right mix of strategists, data engineers, and architects. Our pod model means you get a cross-functional team that owns your engagement end to end, with an average of 30+ years of enterprise experience on each team.

Ready to turn your data into a competitive advantage?

Tell us where you are today and we will show you what is possible. No pitch, just a conversation about your data and your goals.